Central dogma of molecular biology

Phase separation in gene transcription control

Phase separation provides a general mechanism for the formation of biomolecular condensates, and it plays a vital role in regulating diverse cellular processes, including gene expression. Although the role of transcription factors and coactivators in regulating transcription has long been understood, how phase separation is involved in this process is just beginning to be explored. In this review, we highlight recent advance in elucidating the molecular mechanisms and functions of transcriptional condensates in gene expression control. We discuss the different condensates formed at each stage of the transcription cycle and how they are dynamically regulated in response to diverse cellular and extracellular cues that cause rapid changes in gene expression. Furthermore, we present new findings regarding the dysregulation of transcription condensates and their implications in human diseases.

Predicting gene and protein expression levels from DNA and protein sequences with Perceiver

BACKGROUND AND OBJECTIVE

The functions of an organism and its biological processes result from the expression of genes and proteins. Therefore quantifying and predicting mRNA and protein levels is a crucial aspect of scientific research. Concerning the prediction of mRNA levels, the available approaches use the sequence upstream and downstream of the Transcription Start Site (TSS) as input to neural networks. The State-of-the-art models (e.g., Xpresso and Basenjii) predict mRNA levels exploiting Convolutional (CNN) or Long Short Term Memory (LSTM) Networks. However, CNN prediction depends on convolutional kernel size, and LSTM suffers from capturing long-range dependencies in the sequence. Concerning the prediction of protein levels, as far as we know, there is no model for predicting protein levels by exploiting the gene or protein sequences.

METHODS

Here, we exploit a new model type (called Perceiver) for mRNA and protein level prediction, exploiting a Transformer-based architecture with an attention module to attend to long-range interactions in the sequences. In addition, the Perceiver model overcomes the quadratic complexity of the standard Transformer architectures. This work's contributions are 1. DNAPerceiver model to predict mRNA levels from the sequence upstream and downstream of the TSS; 2. ProteinPerceiver model to predict protein levels from the protein sequence; 3. Protein&DNAPerceiver model to predict protein levels from TSS and protein sequences.

RESULTS

The models are evaluated on cell lines, mice, glioblastoma, and lung cancer tissues. The results show the effectiveness of the Perceiver-type models in predicting mRNA and protein levels.

CONCLUSIONS

This paper presents a Perceiver architecture for mRNA and protein level prediction. In the future, inserting regulatory and epigenetic information into the model could improve mRNA and protein level predictions. The source code is freely available at https://github.com/MatteoStefanini/DNAPerceiver.

Investigating the Inhibition of FTSJ1, a Tryptophan tRNA-Specific 2'-O-Methyltransferase by NV TRIDs, as a Mechanism of Readthrough in Nonsense Mutated CFTR

Cystic Fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the CFTR gene, coding for the CFTR chloride channel. About 10% of the CFTR gene mutations are "stop" mutations that generate a premature termination codon (PTC), thus synthesizing a truncated CFTR protein. A way to bypass PTC relies on ribosome readthrough, which is the ribosome's capacity to skip a PTC, thus generating a full-length protein. "TRIDs" are molecules exerting ribosome readthrough; for some, the mechanism of action is still under debate. We investigate a possible mechanism of action (MOA) by which our recently synthesized TRIDs, namely NV848, NV914, and NV930, could exert their readthrough activity by in silico analysis and in vitro studies. Our results suggest a likely inhibition of FTSJ1, a tryptophan tRNA-specific 2'-O-methyltransferase.

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.

Improvement of Error Correction in Nonequilibrium Information Dynamics

Errors are inevitable in information processing and transfer. While error correction is widely studied in engineering, the underlying physics is not fully understood. Due to the complexity and energy exchange involved, information transmission should be considered as a nonequilibrium process. In this study, we investigate the effects of nonequilibrium dynamics on error correction using a memoryless channel model. Our findings suggest that error correction improves as nonequilibrium increases, and the thermodynamic cost can be utilized to improve the correction quality. Our results inspire new approaches to error correction that incorporate nonequilibrium dynamics and thermodynamics, and highlight the importance of the nonequilibrium effects in error correction design, particularly in biological systems.

Air Pollution: Role of Extracellular Vesicles-Derived Non-Coding RNAs in Environmental Stress Response

Air pollution has increased over the years, causing a negative impact on society due to the many health-related problems it can contribute to. Although the type and extent of air pollutants are known, the molecular mechanisms underlying the induction of negative effects on the human body remain unclear. Emerging evidence suggests the crucial involvement of different molecular mediators in inflammation and oxidative stress in air pollution-induced disorders. Among these, non-coding RNAs (ncRNAs) carried by extracellular vesicles (EVs) may play an essential role in gene regulation of the cell stress response in pollutant-induced multiorgan disorders. This review highlights EV-transported ncRNAs' roles in physiological and pathological conditions, such as the development of cancer and respiratory, neurodegenerative, and cardiovascular diseases following exposure to various environmental stressors.

A chemo-mechanical model of endoderm movements driving elongation of the amniote hindgut

While mechanical and biochemical descriptions of development are each essential, integration of upstream morphogenic cues with downstream tissue mechanics remains understudied in many contexts during vertebrate morphogenesis. A posterior gradient of Fibroblast Growth Factor (FGF) ligands generates a contractile force gradient in the definitive endoderm, driving collective cell movements to form the hindgut. Here, we developed a two-dimensional chemo-mechanical model to investigate how mechanical properties of the endoderm and transport properties of FGF coordinately regulate this process. We began by formulating a 2-D reaction-diffusion-advection model that describes the formation of an FGF protein gradient due to posterior displacement of cells transcribing unstable Fgf8 mRNA during axis elongation, coupled with translation, diffusion, and degradation of FGF protein. This was used together with experimental measurements of FGF activity in the chick endoderm to inform a continuum model of definitive endoderm as an active viscous fluid that generates contractile stresses in proportion to FGF concentration. The model replicated key aspects of hindgut morphogenesis, confirms that heterogeneous - but isotropic - contraction is sufficient to generate large anisotropic cell movements, and provides new insight into how chemo-mechanical coupling across the mesoderm and endoderm coordinates hindgut elongation with outgrowth of the tailbud.

Can molecular dynamics be used to simulate biomolecular recognition?

There are many problems in biochemistry that are difficult to study experimentally. Simulation methods are appealing due to direct availability of atomic coordinates as a function of time. However, direct molecular simulations are challenged by the size of systems and the time scales needed to describe relevant motions. In theory, enhanced sampling algorithms can help to overcome some of the limitations of molecular simulations. Here, we discuss a problem in biochemistry that offers a significant challenge for enhanced sampling methods and that could, therefore, serve as a benchmark for comparing approaches that use machine learning to find suitable collective variables. In particular, we study the transitions LacI undergoes upon moving between being non-specifically and specifically bound to DNA. Many degrees of freedom change during this transition and that the transition does not occur reversibly in simulations if only a subset of these degrees of freedom are biased. We also explain why this problem is so important to biologists and the transformative impact that a simulation of it would have on the understanding of DNA regulation.

Applications of synthetic biology in medical and pharmaceutical fields

Synthetic biology aims to design or assemble existing bioparts or bio-components for useful bioproperties. During the past decades, progresses have been made to build delicate biocircuits, standardized biological building blocks and to develop various genomic/metabolic engineering tools and approaches. Medical and pharmaceutical demands have also pushed the development of synthetic biology, including integration of heterologous pathways into designer cells to efficiently produce medical agents, enhanced yields of natural products in cell growth media to equal or higher than that of the extracts from plants or fungi, constructions of novel genetic circuits for tumor targeting, controllable releases of therapeutic agents in response to specific biomarkers to fight diseases such as diabetes and cancers. Besides, new strategies are developed to treat complex immune diseases, infectious diseases and metabolic disorders that are hard to cure via traditional approaches. In general, synthetic biology brings new capabilities to medical and pharmaceutical researches. This review summarizes the timeline of synthetic biology developments, the past and present of synthetic biology for microbial productions of pharmaceutics, engineered cells equipped with synthetic DNA circuits for diagnosis and therapies, live and auto-assemblied biomaterials for medical treatments, cell-free synthetic biology in medical and pharmaceutical fields, and DNA engineering approaches with potentials for biomedical applications.

Coding From Binding? Molecular Interactions at the Heart of Translation

The mechanism and the evolution of DNA replication and transcription, the key elements of the central dogma of biology, are fundamentally well explained by the physicochemical complementarity between strands of nucleic acids. However, the determinants that have shaped the third part of the dogma-the process of biological translation and the universal genetic code-remain unclear. We review and seek parallels between different proposals that view the evolution of translation through the prism of weak, noncovalent interactions between biological macromolecules. In particular, we focus on a recent proposal that there exists a hitherto unrecognized complementarity at the heart of biology, that between messenger RNA coding regions and the proteins that they encode, especially if the two are unstructured. Reflecting the idea that the genetic code evolved from intrinsic binding propensities between nucleotides and amino acids, this proposal promises to forge a link between the distant past and the present of biological systems.

GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm

BACKGROUND

The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments.

METHODS

In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions.

RESULTS

To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method.

CONCLUSION

GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.

Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strategy for treating multimodal datasets generated by high-throughput, single-cell genomic assays.

Multi-Omics Research Strategies for Psoriasis and Atopic Dermatitis

Psoriasis and atopic dermatitis (AD) are multifactorial and heterogeneous inflammatory skin diseases, while years of research have yielded no cure, and the costs associated with caring for people suffering from psoriasis and AD are a huge burden on society. Integrating several omics datasets will enable coordinate-based simultaneous analysis of hundreds of genes, RNAs, chromatins, proteins, and metabolites in particular cells, revealing networks of links between various molecular levels. In this review, we discuss the latest developments in the fields of genomes, transcriptomics, proteomics, and metabolomics and discuss how they were used to identify biomarkers and understand the main pathogenic mechanisms underlying these diseases. Finally, we outline strategies for achieving multi-omics integration and how integrative omics and systems biology can advance our knowledge of, and ability to treat, psoriasis and AD.

Defining multiple layers of intratumor heterogeneity based on variations of perturbations in multi-omics profiling

BACKGROUND

Intratumor heterogeneity (ITH) plays a crucial role in tumor progression, relapse, immune evasion, and drug resistance. Existing ITH quantification methods based on a single molecular level are inadequate to capture ITH evolving from genotype to phenotype.

METHODS

We designed a set of information entropy (IE)-based algorithms for quantifying ITH at the genome (somatic copy number alterations and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. We evaluated the performance of these algorithms by analyzing the correlations between their ITH scores and ITH-associated molecular and clinical features in 33 TCGA cancer types. Moreover, we evaluated the correlations between the ITH measures at different molecular levels by Spearman correlation and clustering analysis.

RESULTS

The IE-based ITH measures had significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein-level ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Clustering analysis based on the ITH scores identified four subtypes of pan-cancer showing significantly different prognosis. Finally, the ITH integrating the seven ITH measures displayed more prominent properties of ITH than that at a single level.

CONCLUSIONS

This analysis provides landscapes of ITH at various molecular levels. Combining the ITH observation from different molecule levels will improve personalized management for cancer patients.

Arabidopsis HOT3/eIF5B1 constrains rRNA RNAi by facilitating 18S rRNA maturation

Ribosome biogenesis is essential for protein synthesis in gene expression. Yeast eIF5B has been shown biochemically to facilitate 18S ribosomal RNA (rRNA) 3' end maturation during late-stage 40S ribosomal subunit assembly and gate the transition from translation initiation to elongation. But the genome-wide effects of eIF5B have not been studied at the single-nucleotide resolution in any organism, and 18S rRNA 3' end maturation is poorly understood in plants. Arabidopsis HOT3/eIF5B1 was found to promote development and heat stress acclimation by translational regulation, but its molecular function remained unknown. Here, we show that HOT3 is a late-stage ribosome biogenesis factor that facilitates 18S rRNA 3' end processing and is a translation initiation factor that globally impacts the transition from initiation to elongation. By developing and implementing 18S-ENDseq, we revealed previously unknown events in 18S rRNA 3' end maturation or metabolism. We quantitatively defined processing hotspots and identified adenylation as the prevalent nontemplated RNA addition at the 3' ends of pre-18S rRNAs. Aberrant 18S rRNA maturation in hot3 further activated RNA interference to generate RDR1- and DCL2/4-dependent risiRNAs mainly from a 3' portion of 18S rRNA. We further showed that risiRNAs in hot3 were predominantly localized in ribosome-free fractions and were not responsible for the 18S rRNA maturation or translation initiation defects in hot3. Our study uncovered the molecular function of HOT3/eIF5B1 in 18S rRNA maturation at the late 40S assembly stage and revealed the regulatory crosstalk among ribosome biogenesis, messenger RNA (mRNA) translation initiation, and siRNA biogenesis in plants.

Current Methods for Identifying Plasma Membrane Proteins as Cancer Biomarkers

Plasma membrane proteins are a special class of biomolecules present on the cellular membrane. They provide the transport of ions, small molecules, and water in response to internal and external signals, define a cell's immunological identity, and facilitate intra- and intercellular communication. Since they are vital to almost all cellular functions, their mutants, or aberrant expression is linked to many diseases, including cancer, where they are a part of cancer cell-specific molecular signatures and phenotypes. In addition, their surface-exposed domains make them exciting biomarkers for targeting by imaging agents and drugs. This review looks at the challenges in identifying cancer-related cell membrane proteins and the current methodologies that solve most of the challenges. We classified the methodologies as biased, i.e., search cells for the presence of already known membrane proteins. Second, we discuss the unbiased methods that can identify proteins without prior knowledge of what they are. Finally, we discuss the potential impact of membrane proteins on the early detection and treatment of cancer.

The Long Road to a Synthetic Self-Replicating Central Dogma

The construction of a biochemical system capable of self-replication is a key objective in bottom-up synthetic biology. Throughout the past two decades, a rapid progression in the design of in vitro cell-free systems has provided valuable insight into the requirements for the development of a minimal system capable of self-replication. The main limitations of current systems can be attributed to their macromolecular composition and how the individual macromolecules use the small molecules necessary to drive RNA and protein synthesis. In this Perspective, we discuss the recent steps that have been taken to generate a minimal cell-free system capable of regenerating its own macromolecular components and maintaining the homeostatic balance between macromolecular biogenesis and consumption of primary building blocks. By following the flow of biological information through the central dogma, we compare the current versions of these systems to date and propose potential alterations aimed at designing a model system for self-replicative synthetic cells.

An emerging class of nucleic acid-sensing regulators in bacteria: WYL domain-containing proteins

Transcriptional regulation plays a central role in adaptation to changing environments for all living organisms. Recently, proteins belonging to a novel widespread class of bacterial transcription factors have been characterized in mycobacteria and Proteobacteria. Those multidomain proteins carry a WYL domain that is almost exclusive to the domain of bacteria. WYL domain-containing proteins act as regulators in different cellular contexts, including the DNA damage response and bacterial immunity. WYL domains have an Sm-like fold with five antiparallel β-strands arranged into a β-sandwich preceded by an α-helix. A common feature of WYL domains is their ability to bind nucleic acids that regulate their activity. In this review, we discuss recent progress made toward the understanding of WYL domain-containing proteins as transcriptional regulators, their structural features, and molecular mechanisms, as well as their functional roles in bacterial physiology.

Association of genetic variants of CircCHST15 with oral squamous cell carcinoma in the Chinese Han population

BACKGROUND

Oral squamous cell carcinoma (OSCC) is the most common cancer in the oral cavity. The relationship between the genetic susceptibility of circCHST15 and OSCC remains unclear.

METHODS

Genetic variants of circCHST15 were screened using a genotyping analysis from 1044 patients with OSCC and 3199 healthy participants. The circCHST15 expression was detected in 32 pairs of OSCC tissues. The circular RNA quantitative trait locus analysis and the reporter gene assay were performed for verification.

RESULTS

The circCHST15 expression was upregulated in OSCC (Wilcoxon p < 1e-3). The genotyping analysis screened out 61 loci in circCHST15 associated with the risk of OSCC. After adjustment and annotation, rs28707473 (A > C, odds ratio = 1.21, 95% CI: 1.076-1.361, p = 1.453e-3) was selected. This genetic variation could elevate the circCHST15 expression level possibly by altering the structure of circular RNAs and affecting transcription factor binding.

CONCLUSIONS

The results of this study suggested that genetic variants of circCHST15 may contribute to OSCC susceptibility.

The uprise of RNA biology in neuroendocrine neoplasms: altered splicing and RNA species unveil translational opportunities

Neuroendocrine neoplasms (NENs) comprise a highly heterogeneous group of tumors arising from the diffuse neuroendocrine system. NENs mainly originate in gastrointestinal, pancreatic, and pulmonary tissues, and despite being rare, show rising incidence. The molecular mechanisms underlying NEN development are still poorly understood, although recent studies are unveiling their genomic, epigenomic and transcriptomic landscapes. RNA was originally considered as an intermediary between DNA and protein. Today, compelling evidence underscores the regulatory relevance of RNA processing, while new RNA molecules emerge with key functional roles in core cell processes. Indeed, correct functioning of the interrelated complementary processes comprising RNA biology, its processing, transport, and surveillance, is essential to ensure adequate cell homeostasis, and its misfunction is related to cancer at multiple levels. This review is focused on the dysregulation of RNA biology in NENs. In particular, we survey alterations in the splicing process and available information implicating the main RNA species and processes in NENs pathology, including their role as biomarkers, and their functionality and targetability. Understanding how NENs precisely (mis)behave requires a profound knowledge at every layer of their heterogeneity, to help improve NEN management. RNA biology provides a wide spectrum of previously unexplored processes and molecules that open new avenues for NEN detection, classification and treatment. The current molecular biology era is rapidly evolving to facilitate a detailed comprehension of cancer biology and is enabling the arrival of personalized, predictive and precision medicine to rare tumors like NENs.

Could interventions on physical activity mitigate genomic liability for obesity? Applying the health disparity framework in genetically informed studies

Polygenic scores (PGS) are now commonly available in longitudinal cohort studies, leading to their integration into epidemiological research. In this work, our aim is to explore how polygenic scores can be used as exposures in causal inference-based methods, specifically mediation analyses. We propose to estimate the extent to which the association of a polygenic score indexing genetic liability to an outcome could be mitigated by a potential intervention on a mediator. To do this this, we use the interventional disparity measure approach, which allows us to compare the adjusted total effect of an exposure on an outcome, with the association that would remain had we intervened on a potentially modifiable mediator. As an example, we analyse data from two UK cohorts, the Millennium Cohort Study (MCS, N = 2575) and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 3347). In both, the exposure is genetic liability for obesity (indicated by a PGS for BMI), the outcome is late childhood/early adolescent BMI, and the mediator and potential intervention target is physical activity, measured between exposure and outcome. Our results suggest that a potential intervention on child physical activity can mitigate some of the genetic liability for childhood obesity. We propose that including PGSs in a health disparity measure approach, and causal inference-based methods more broadly, is a valuable addition to the study of gene-environment interplay in complex health outcomes.

Synthesising a minimal cell with artificial metabolic pathways

A "synthetic minimal cell" is considered here as a cell-like artificial vesicle reproduction system in which a chemical and physico-chemical transformation network is regulated by information polymers. Here we synthesise such a minimal cell consisting of three units: energy production, information polymer synthesis, and vesicle reproduction. Supplied ingredients are converted to energy currencies which trigger the synthesis of an information polymer, where the vesicle membrane plays the role of a template. The information polymer promotes membrane growth. By tuning the membrane composition and permeability to osmolytes, the growing vesicles show recursive reproduction over several generations. Our "synthetic minimal cell" greatly simplifies the scheme of contemporary living cells while keeping their essence. The chemical pathways and the vesicle reproduction pathways are well described by kinetic equations and by applying the membrane elasticity model, respectively. This study provides new insights to better understand the differences and similarities between non-living forms of matter and life.

Interaction of an anticancer benzopyrane derivative with DNA: Biophysical, biochemical, and molecular modeling studies

BACKGROUND

SIMR1281 is a potent anticancer lead candidate with multi- target activity against several proteins; however, its mechanism of action at the molecular level is not fully understood. Revealing the mechanism and the origin of multitarget activity is important for the rational identification and optimization of multitarget drugs.

METHODS

We have used a variety of biophysical (circular dichroism, isothermal titration calorimetry, viscosity, and UV DNA melting), biochemical (topoisomerase I & II assays) and computational (molecular docking and MD simulations) methods to study the interaction of SIMR1281 with duplex DNA structures.

RESULTS

The biophysical results revealed that SIMR1281 binds to dsDNA via an intercalation-binding mode with an average binding constant of 3.1 × 10⁶ M^-1. This binding mode was confirmed by the topoisomerases' inhibition assays and molecular modeling simulations, which showed the intercalation of the benzopyrane moiety between DNA base pairs, while the remaining moieties (thiazole and phenyl rings) sit in the minor groove and interact with the flanking base pairs adjacent to the intercalation site.

CONCLUSIONS

The DNA binding characteristics of SIMR1281, which can disrupt/inhibit DNA function as confirmed by the topoisomerases' inhibition assays, indicate that the observed multi-target activity might originate from ligand intervention at nucleic acids level rather than due to direct interactions with multiple biological targets at the protein level.

GENERAL SIGNIFICANCE

The findings of this study could be helpful to guide future optimization of benzopyrane-based ligands for therapeutic purposes.

A universal method for analyzing copolymer growth

Polymers consisting of more than one type of monomer, known as copolymers, are vital to both living and synthetic systems. Copolymerization has been studied theoretically in a number of contexts, often by considering a Markov process in which monomers are added or removed from the growing tip of a long copolymer. To date, the analysis of the most general models of this class has necessitated simulation. We present a general method for analyzing such processes without resorting to simulation. Our method can be applied to models with an arbitrary network of sub-steps prior to addition or removal of a monomer, including non-equilibrium kinetic proofreading cycles. Moreover, the approach allows for a dependency of addition and removal reactions on the neighboring site in the copolymer and thermodynamically self-consistent models in which all steps are assumed to be microscopically reversible. Using our approach, thermodynamic quantities such as chemical work; kinetic quantities such as time taken to grow; and statistical quantities such as the distribution of monomer types in the growing copolymer can be directly derived either analytically or numerically from the model definition.

Ribosome profiling reveals the translational landscape and allele-specific translational efficiency in rice

Translational regulation is a critical step in the process of gene expression and governs the synthesis of proteins from mRNAs. Many studies have revealed translational regulation in plants in response to various environmental stimuli. However, there have been no studies documenting the comprehensive landscape of translational regulation and allele-specific translational efficiency in multiple plant tissues, especially those of rice, a main staple crop that feeds nearly half of the world's population. Here we used RNA sequencing and ribosome profiling data to analyze the transcriptome and translatome of an elite hybrid rice, Shanyou 63 (SY63), and its parental varieties Zhenshan 97 and Minghui 63. The results revealed that gene expression patterns varied more among tissues than among varieties at the transcriptional and translational levels. We identified 3392 upstream open reading frames (uORFs), and the uORF-containing genes were enriched in transcription factors. Only 668 of 13 492 long non-coding RNAs could be translated into peptides. Finally, we discovered numerous genes with allele-specific translational efficiency in SY63 and demonstrated that some cis-regulatory elements may contribute to allelic divergence in translational efficiency. Overall, these findings may improve our understanding of translational regulation in rice and provide information for molecular breeding research.

Mass Nanotags Mediate Parallel Amplifications on Nanointerfaces for Multiplexed Profiling of RNAs

Multiplexed profiling of RNAs aids in a comprehensive understanding of multiparameter-defined cellular processes and pathological states. We herein present a mass nanotags-enabled interfacial assembly system (MNTs-AS) with parallel amplification motors for simultaneous assaying of multiple RNAs in biosystems by matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). Four kinds of MNTs encoding corresponding RNA can be cyclically assembled on magnetic beads by target-triggered catalytic hairpin assembly (CHA) machineries on nanointerfaces, generating multiplexed and amplified characteristic ion signals assigned to target RNAs upon MALDI MS interrogation. By virtue of high sensitivity and multiplexing capability, the MNTs-AS-based MS assay allows precision subtyping of diverse breast cancer cells and their exosomes by multiplexed profiling of miRNA-21, miRNA-373, miRNA-155, and manganese superoxide dismutase mRNA via a single MS inquiry. This method provides a promising tool for unraveling multiple RNA-involved biological events in fundamental research and distinguishing different cancer subtypes in clinical practice.

Applications of Genomics and Transcriptomics in Precision Medicine for Myopia Control or Prevention

Myopia is a globally emerging concern accompanied by multiple medical and socio-economic burdens with no well-established causal treatment to control thus far. The study of the genomics and transcriptomics of myopia treatment is crucial to delineate disease pathways and provide valuable insights for the design of precise and effective therapeutics. A strong understanding of altered biochemical pathways and underlying pathogenesis leading to myopia may facilitate early diagnosis and treatment of myopia, ultimately leading to the development of more effective preventive and therapeutic measures. In this review, we summarize current data about the genomics and transcriptomics of myopia in human and animal models. We also discuss the potential applicability of these findings to precision medicine for myopia treatment.

Intercepting biological messages: Antibacterial molecules targeting nucleic acids during interbacterial conflicts

Bacteria live in polymicrobial communities and constantly compete for resources. These organisms have evolved an array of antibacterial weapons to inhibit the growth or kill competitors. The arsenal comprises antibiotics, bacteriocins, and contact-dependent effectors that are either secreted in the medium or directly translocated into target cells. During bacterial antagonistic encounters, several cellular components important for life become a weak spot prone to an attack. Nucleic acids and the machinery responsible for their synthesis are well conserved across the tree of life. These molecules are part of the information flow in the central dogma of molecular biology and mediate long- and short-term storage for genetic information. The aim of this review is to summarize the diversity of antibacterial molecules that target nucleic acids during antagonistic interbacterial encounters and discuss their potential to promote the emergence antibiotic resistance.

Modeling mRNA Translation With Ribosome Abortions

We derive a deterministic mathematical model for the flow of ribosomes along a mRNA called the ribosome flow model with extended objects and abortions (RFMEOA). This model incorporates important cellular features such as every ribosome covers several codons and they may detach from various regions along the track due to more realistic biological situations including phenomena of ribosome-ribosome collisions. We prove that the ribosome density profile along the mRNA in the RFMEOA and in particular, the protein production rate converge to a unique steady-state. Simulations of the RFMEOA demonstrate a surprising result that an increase in the initiation rate may sometimes lead to a decrease in the production rate. We believe that this model could be helpful to provide insight into the effects of premature termination on the protein expression and be useful for understanding and re-engineering the translation process.

Assessing how well students understand the molecular basis of evolution by natural selection

Researchers have called for undergraduate courses to update teaching frameworks based on the Modern Synthesis with insights from molecular biology, by stressing the molecular underpinnings of variation and adaptation. To support this goal, we developed a modified version of the widely used Assessing Conceptual Reasoning of Natural Selection (ACORNS) instrument. The expanded tool, called the E-ACORNS, is explicitly designed to test student understanding of the connections among genotypes, phenotypes, and fitness. E-ACORNS comprises a slight modification to the ACORNS open-response prompts and a new scoring rubric. The rubric is based on five core concepts in evolution by natural selection, with each concept broken into elements at the novice, intermediate, and expert-level understanding. Initial tests of the E-ACORNS showed that (1) upper-level undergraduates can score responses reliably and quickly, and (2) students who were just starting an introductory biology series for majors do not yet grasp the molecular basis of phenotypic variation and its connection to fitness.

The genome editing revolution

A series of spectacular scientific discoveries and technological advances in the second half of the 20th century have provided the basis for the ongoing genome editing revolution. The elucidation of structural and functional features of DNA and RNA was followed by pioneering studies on genome editing: Molecular biotechnology was born. Since then, four decades followed during which progress of scientific insights and technological methods continued at an overwhelming pace. Fundamental insights into microbial host-virus interactions led to the development of tools for genome editing using restriction enzymes or the revolutionary CRISPR-Cas technology. In this review, we provide a historical overview of milestones that led to the genome editing revolution and speculate about future trends in biotechnology.

Reverse Transcriptases: From Discovery and Applications to Xenobiology

Reverse transcriptases are DNA polymerases that can use RNA as a template for DNA synthesis. They thus catalyze the reverse of transcription. Although discovered in 1970, reverse transcriptases are still of great interest and are constantly being further developed for numerous modern research approaches. They are frequently used in biotechnological and molecular diagnostic applications. In this review, we describe the discovery of these fascinating enzymes and summarize research results and applications ranging from molecular cloning, direct virus detection, and modern sequencing methods to xenobiology.

IARA: a complete and curated atlas of the biogenesis of spliceosome machinery during RNA splicing

Splicing is one of the most important post-transcriptional processing systems and is responsible for the generation of transcriptome diversity in all living eukaryotes. Splicing is regulated by the spliceosome machinery, which is responsible for each step of primary RNA processing. However, current molecules and stages involved in RNA splicing are still spread over different studies. Thus, a curated atlas of spliceosome-related molecules and all involved stages during RNA processing can provide all researchers with a reliable resource to better investigate this important mechanism. Here, we present IARA (website access: https://pucpr-bioinformatics.github.io/atlas/), an extensively curated and constantly updated catalog of molecules involved in spliceosome machinery. IARA has a map of the steps involved in the human splicing mechanism, and it allows a detailed overview of the molecules involved throughout the distinct steps of splicing.

Epitranscriptomics in metabolic disease

While epigenetic modifications of DNA and histones play main roles in gene transcription regulation, recently discovered post-transcriptional RNA modifications, known as epitranscriptomic modifications, have been found to have a profound impact on gene expression by regulating RNA stability, localization and decoding efficiency. Importantly, genetic variations or environmental perturbations of epitranscriptome modifiers (that is, writers, erasers and readers) are associated with obesity and metabolic diseases, such as type 2 diabetes. The epitranscriptome is closely coupled to epigenetic signalling, adding complexity to our understanding of gene expression in both health and disease. Moreover, the epitranscriptome in the parental generation can affect organismal phenotypes in the next generation. In this Review, we discuss the relationship between epitranscriptomic modifications and metabolic diseases, their relationship with the epigenome and possible therapeutic strategies.

Mathematical and physical considerations indicating that the cell genome is a read-write memory

The molecular mechanisms that govern biological evolution have not been fully elucidated so far. Recent studies indicate that regulatory proteins, acting as decision-making complex devices, can accelerate or retard the evolution of cells. Such biochemically controlled evolution may be considered as an optimization process of logical nature aimed at developing fitter species that can better survive in a specific environment. Therefore, we may assume that new genetic information can be stored in the cell memory (i.e., genome) by a sophisticated biomolecular process that resembles writing in computer memory. Such a hypothesis is theoretically supported by a recent work showing that logic is a necessary component of life, so living systems process information in the same way as computers. The current study summarizes existing evidence showing that cells can intentionally modify their stored data by biochemical processes resembling stochastic algorithms to avoid environmental stress and increase their chances of survival. Furthermore, the mathematical and physical considerations that render a read-write memory a necessary component of biological systems are presented.

Transcriptomic, proteomic, and functional consequences of codon usage bias in human cells during heterologous gene expression

Differences in codon frequency between genomes, genes, or positions along a gene, modulate transcription and translation efficiency, leading to phenotypic and functional differences. Here, we present a multiscale analysis of the effects of synonymous codon recoding during heterologous gene expression in human cells, quantifying the phenotypic consequences of codon usage bias at different molecular and cellular levels, with an emphasis on translation elongation. Six synonymous versions of an antibiotic resistance gene were generated, fused to a fluorescent reporter, and independently expressed in HEK293 cells. Multiscale phenotype was analyzed by means of quantitative transcriptome and proteome assessment, as proxies for gene expression; cellular fluorescence, as a proxy for single-cell level expression; and real-time cell proliferation in absence or presence of antibiotic, as a proxy for the cell fitness. We show that differences in codon usage bias strongly impact the molecular and cellular phenotype: (i) they result in large differences in mRNA levels and protein levels, leading to differences of over 15 times in translation efficiency; (ii) they introduce unpredicted splicing events; (iii) they lead to reproducible phenotypic heterogeneity; and (iv) they lead to a trade-off between the benefit of antibiotic resistance and the burden of heterologous expression. In human cells in culture, codon usage bias modulates gene expression by modifying mRNA availability and suitability for translation, leading to differences in protein levels and eventually eliciting functional phenotypic changes.

On the origin and evolution of RNA editing in metazoans

Extensive adenosine-to-inosine (A-to-I) editing of nuclear-transcribed mRNAs is the hallmark of metazoan transcriptional regulation. Here, by profiling the RNA editomes of 22 species that cover major groups of Holozoa, we provide substantial evidence supporting A-to-I mRNA editing as a regulatory innovation originating in the last common ancestor of extant metazoans. This ancient biochemistry process is preserved in most extant metazoan phyla and primarily targets endogenous double-stranded RNA (dsRNA) formed by evolutionarily young repeats. We also find intermolecular pairing of sense-antisense transcripts as an important mechanism for forming dsRNA substrates for A-to-I editing in some but not all lineages. Likewise, recoding editing is rarely shared across lineages but preferentially targets genes involved in neural and cytoskeleton systems in bilaterians. We conclude that metazoan A-to-I editing might first emerge as a safeguard mechanism against repeat-derived dsRNA and was later co-opted into diverse biological processes due to its mutagenic nature.

Molecular elements: novel approaches for molecular building

Classically, a molecular element (ME) is a pure substance composed of two or more atoms of the same element. However, MEs, in the context of this review, can be any molecules as elements bonded together into the backbone of synthetic oligonucleotides (ONs) with designed sequences and functions, including natural A, T, C, G, U, and unnatural bases. The use of MEs can facilitate the synthesis of designer molecules and smart materials. In particular, we discuss the landmarks associated with DNA structure and related technologies, as well as the extensive application of ONs, the ideal type of molecules for intervention therapy aimed at correcting disease-causing genetic errors (indels). It is herein concluded that the discovery of ON therapeutics and the fabrication of designer molecules or nanostructures depend on the ME concept that we previously published. Accordingly, ME will be our focal point as we discuss related research directions and perspectives in making molecules and materials. This article is part of the theme issue 'Reactivity and mechanism in chemical and synthetic biology'.

Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.

Lichtgesteuerte Translation von mRNA in Eukaryoten

Messenger RNA (mRNA) shows great potential for medical applications, as recently demonstrated by the mRNA-based vaccines against the coronavirus. In addition, it has long been used for ectopic gene expression in cells and model organisms. While numerous methodologies are available for controlling gene expression at the level of transcription, approaches to control translation are scarce. Here we review strategies for direct light-mediated activation of mRNA translation via photocleavable groups and their potential to achieve spatial and temporal control of protein production.

Template-Assembled Synthetic G-Quartets (TASQs): multiTASQing Molecular Tools for Investigating DNA and RNA G-Quadruplex Biology

Biomimetics is defined as a "practice of making technological design that copies natural processes", with the idea that "nature has already solved the challenges we are trying to solve" (Cambridge Dictionary). The challenge we decided to address several years ago was the selective targeting of G quadruplexes (G4s) by small molecules (G4 ligands). Why? Because G4s, which are four-stranded DNA and RNA structures that fold from guanine (G)-rich sequences, are suspected to play key biological roles in human cells and diseases. Selective G4 ligands can thus be used as small-molecule modulators to gain a deep understanding of cell circuitry where G4s are involved, thus complying with the very definition of chemical biology (Stuart Schreiber) applied here to G4 biology. How? Following a biomimetic approach that hinges on the observation that G4s are stable secondary structures owing to the ability of Gs to self-associate to form G quartets, and then of G quartets to self-stack to form the columnar core of G4s. Therefore, using a synthetic G quartet as a G4 ligand represents a unique example of biomimetic recognition of G4s.We formulated this hypothesis more than a decade ago, stepping on years of research on Gs, G4s, and G4 ligands. Our approach led to the design, synthesis, and use of a broad family of synthetic G quartets, also referred to as TASQs for template-assembled synthetic G quartets (John Sherman). This quest led us across various chemical lands (organic and supramolecular chemistry, chemical biology, and genetics), along a route on which every new generation of TASQ was a milestone in the growing portfolio of ever smarter molecular tools to decipher G4 biology. As discussed in this Account, we detail how and why we successively develop the very first prototypes of (i) biomimetic ligands, which interact with G4s according to a bioinspired, like-likes-like interaction between two G quartets, one from the ligand, the other from the G4; (ii) smart ligands, which adopt their active conformation only in the presence of their G4 targets; (iii) twice-as-smart ligands, which act as both smart ligands and smart fluorescent probes, whose fluorescence is triggered (turned on) upon interaction with their G4 targets; and (iv) multivalent ligands, which display additional functionalities enabling the detection, isolation, and identification of G4s both in vitro and in vivo. This quest led us to gather a panel of 14 molecular tools which were used to investigate the biology of G4s at a cellular level, from basic optical imaging to multiomics studies.

Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops

The accurate "base pairing" in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar's advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.

Single mRNA Imaging with Fluorogenic RNA Aptamers and Small-molecule Fluorophores

Messenger RNA (mRNA) is the fundamental information transfer system in the cell. Tracking single mRNA from transcription to degradation with fluorescent probes provides spatiotemporal information in cells about how the genetic information is transferred from DNA to proteins. The traditional single mRNA imaging approach utilizes RNA hairpins (e.g. MS2) and tethered fluorescent protein as probes. As an exciting alternative, RNA aptamers: small-molecule fluorophores (SFs) systems have emerged as novel single mRNA imaging probes since 2019, exhibiting several advantages including fluorogenic ability and minimal perturbation. This review summarizes all five reported RNA aptamers: SFs systems for single mRNA imaging in living cells so far. It also discusses the challenges and provides prospects for single mRNA imaging applications. This review is expected to inspire researchers to develop RNA aptamers: SFs systems for studying gene expression at single-molecule resolution in cells.

Dealing with dimensionality: the application of machine learning to multi-omics data

MOTIVATION

Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets.

RESULTS

Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments.

AVAILABILITY AND IMPLEMENTATION

All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering the multi-scale, quantitative cis-regulatory code

Uncovering the cis-regulatory code that governs when and how much each gene is transcribed in a given genome and cellular state remains a central goal of biology. Here, we discuss major layers of regulation that influence how transcriptional outputs are encoded by DNA sequence and cellular context. We first discuss how transcription factors bind specific DNA sequences in a dosage-dependent and cooperative manner and then proceed to the cofactors that facilitate transcription factor function and mediate the activity of modular cis-regulatory elements such as enhancers, silencers, and promoters. We then consider the complex and poorly understood interplay of these diverse elements within regulatory landscapes and its relationships with chromatin states and nuclear organization. We propose that a mechanistically informed, quantitative model of transcriptional regulation that integrates these multiple regulatory layers will be the key to ultimately cracking the cis-regulatory code.

Emerging and established therapies for chemotherapy-induced ototoxicity

PURPOSE

Ototoxicity is considered a dose-limiting side effect of some chemotherapies. Hearing loss, in particular, can have significant implications for the quality of life for cancer survivors. Here, we review therapeutic approaches to mitigating ototoxicity related to chemotherapy.

METHODS

Literature review.

CONCLUSIONS

Numerous otoprotection strategies are undergoing active investigation. However, numerous challenges exist to confer adequate protection while retaining the anti-cancer efficacy of the chemotherapy.

IMPLICATIONS FOR CANCER SURVIVORS

Ototoxicity can have significant implications for cancer survivors, notably those receiving cisplatin. Clinical translation of multiple otoprotection approaches will aid in limiting these consequences.

Overview of gene expression techniques with an emphasis on vitamin D related studies

Each cell controls when and how its genes must be expressed for proper function. Every function in a cell is driven by signaling molecules through various regulatory cascades. Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA. The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties. Malfunction in gene expression harms the cell and can lead to the development of various disease conditions. The use of rapid high-throughput gene expression profiling unravels the complexity of human disease at various levels. Peripheral blood mononuclear cells (PBMC) have been used frequently to understand gene expression homeostasis in various disease conditions. However, more studies are required to validate whether PBMC gene expression patterns accurately reflect the expression of other cells or tissues. Vitamin D, which is responsible for a multitude of health consequences, is also an immune modulatory hormone with major biological activities in the innate and adaptive immune systems. Vitamin D exerts its diverse biological effects in target tissues by regulating gene expression and its deficiency, is recognized as a public health problem worldwide. Understanding the genetic factors that affect vitamin D has the potential benefit that it will make it easier to identify individuals who require supplementation. Different technological advances in gene expression can be used to identify and assess the severity of disease and aid in the development of novel therapeutic interventions. This review focuses on different gene expression approaches and various clinical studies of vitamin D to investigate the role of gene expression in identifying the molecular signature of the disease.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

Cells as the first data scientists

The concepts that we generally associate with the field of data science are strikingly descriptive of the way that life, in general, processes information about its environment. The 'information life cycle', which enumerates the stages of information treatment in data science endeavours, also captures the steps of data collection and handling in biological systems. Similarly, the 'data-information-knowledge ecosystem', developed to illuminate the role of informatics in translating raw data into knowledge, can be a framework for understanding how information is constantly being transferred between life and the environment. By placing the principles of data science in a broader biological context, we see the activities of data scientists as the latest development in life's ongoing journey to better understand and predict its environment. Finally, we propose that informatics frameworks can be used to understand the similarities and differences between abiotic complex evolving systems and life.

Biophysical Reviews: Turning the page from 2022 to 2023

This Editorial (vol. 15 issue 1-Regular Issue featuring an Issue Focus on the "100th Anniversary of Har Gobind Khorana") first describes the issue contents before providing both, a look back at some journal highlights from 2022, and a look forward to what we can expect from 2023. The Editorial closes with a roundup of new journal access features and an acknowledgement of those supporting the journal.