Central Dogma Research Articles

MicroRNA mediates the effects of food factors.

Food factors elicit physiological effects by interfering with the central dogma system, including DNA methylation, replication, transcription, and translation. MicroRNAs (miRNAs) are non-coding short RNAs that are approximately 20 nucleotides long and play a crucial role in the regulation of mRNA levels and translation processes. Importantly, miRNAs can be delivered to different locations in nanovesicles. However, little is known about their roles as mediators of the effects of food factors. This review introduces recent findings on the role of miRNAs in the beneficial effects of food factors, including green tea polyphenols and soybean isoflavones, and discusses the importance of miRNAs as mediators of the beneficial effects of food.

Integration of Health Care Modules in Current Health Care Management Systems in India

Health care beyond worldwide boundaries is a critical problem, particularly when the population differs in terms of geographic position, culture, habits, socioeconomic background, medical strategy, and variations in disease burden over time. For countries like India, with a significant percentage of high-end medical experts as well as support and other health care systems, the development and integration of comprehensive and functionally versatile health care management systems are among the most important clinical problems addressed. The central dogma of a country that facilitates adopting a conglomeration will create a dedicated population to provide tailor-made health care in health shield, health protection, and health care delivery; a nation that takes care of a nation. Until now, it has given us E-Health, and offerings have been fine-tuned in research literature. In India, the health care domain is currently breaking the boundaries of knowledge workers in clinical and research domains working with the help of these workhorses (Fletcher-Brown et al. 2021). Many e-governance and public health reform programs in India are under consideration, and many of them are under implementation. This research focuses more on the integration of health care technology relevant to the paper topic within clinical setups, passive provision, and utilization of services for administrative purposes technologically implemented (Das et al. 2023).

MSphere of Influence: Revisiting the central dogma, again!

Dr. Parimal Samir works in the field of host-pathogen interactions. In this mSphere of Influence article, he reflects on how the manuscript entitled "De novo gene synthesis by an antiviral reverse transcriptase" by Samuel Sternberg and colleagues made an impact by reminding him that there is still so much to discover in life sciences.

Molecular Dynamics Simulation Insights into Hydrogen Bonding‐Mediated Conformational Changes Topoisomerase‐IB

AbstractTopoisomerase‐IB changes the topological state of DNA during central dogma by the formation of intermediate bond in between active amino acid and DNA strand. How the hydrogen bonding provides hooks restriction during the topological change and gives the conformational change. This study employs a 200 ns Molecular Dynamics (MD) simulation to analyze the conformational changes of Topo‐IB resulting from artificially created intermediate bonds. Specifically, the study focuses on hydrogen bonding interactions between Tyrosine at position Y509 in the protein and Thymine at DT561 in the DNA. The investigation reveals that these artificial intermediate bonds act as temporary hooks, stabilizing the complex's structure, and driving significant conformational changes. The analysis of Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) values underscores the dynamic nature of the complex, with artificial intermediate bonds playing a pivotal role.

Progress and challenges in human developmental cell atlas.

Illustrating molecular mechanisms of human embryonic development has always been one of the most significant challenges in biology. The scarcity of human embryo samples, the difficulty in dissecting embryo samples, and the complex structures of human organs are the major obstacles in studying human embryogenesis. In recent years, with the rapid advancement of single-cell technology, humans can systematically analyze the dynamic changes in differentiation at various stages of the central dogma and achieve observation and research with spatial information. This has accelerated the progress in constructing a human developmental cell atlas, ultimately allowing us to depict the cell ontology, fate trajectories, and three-dimensional dynamic changes of human development. In this review, we first introduce the single-cell technologies used to construct the atlas, then summarize the latest progress in human developmental cell atlas, followed by identifying the main problems and challenges in this field so far. Finally, we discuss how to utilize the human developmental cell atlas to address key biological and medical issues. This review provides guidance for the optimal use of single-cell omics technology in constructing and applying a human developmental cell atlas.

Impact of Chromatin Organization and Epigenetics on CRISPR-Cas and TALEN Genome Editing.

DNA lies at the heart of the central dogma of life. Altering DNA can modify the flow of information in fundamental cellular processes such as transcription and translation. The ability to precisely manipulate DNA has led to remarkable advances in treating incurable human genetic ailments and has changed the landscape of biological research. Genome editors such as CRISPR-Cas nucleases and TALENs have become ubiquitous tools in basic and applied biological research and have been translated to the clinic to treat patients. The specificity and modularity of these genome editors have made it possible to efficiently engineer genomic DNA; however, underlying principles governing editing outcomes in eukaryotes are still being uncovered. Editing efficiency can vary from cell type to cell type for the same DNA target sequence, necessitating de novo design and validation efforts. Chromatin structure and epigenetic modifications have been shown to affect the activity of genome editors because of the role they play in hierarchical organization of the underlying DNA. Understanding the nuclear search mechanism of genome editors and their molecular interactions with higher order chromatin will lead to improved models for predicting precise genome editing outcomes. Insights from such studies will unlock the entire genome to be engineered for the creation of novel therapies to treat critical illnesses.

Lysine methylation steps into another step of the central dogma.

Lysine methylation steps into another step of the central dogma.

Striving for clarity in language about gene expression.

What do we mean when we say 'gene expression'? In the decades following Crick's 1958 central dogma of molecular biology, whereby genetic information flows from DNA (genes) to RNA (transcripts) to protein (products), we have learned a great deal about DNA, RNA, proteins, and the ensuing phenotypic changes. With the advent of high-throughput technologies (1990s), molecular biologists and computer scientists forged critical collaborations to understand the vast amount of data being generated, rapidly escalating gene expression research to the 'omics' level: entire sets of genes (genomes), transcribed RNAs (transcriptomes), and synthesized proteins (proteomes). However, some concessions came to be made for molecular biologists and computer scientists to understand each other-one of the most prevalent being the increasingly widespread use of 'gene' to mean 'RNAs originating from a DNA segment'. This loosening of terminology, we will argue, creates ambiguity and confusion. We propose guidelines to increase precision and clarity when communicating about gene expression, most notably to reserve 'gene' for the DNA template and 'transcript' for the RNA transcribed from that gene. Striving to use perspicuous terminology will promote rigorous gene expression science and accelerate discovery in this highly promising area of biology.

The Enriched Central Dogma

The Enriched Central Dogma

Are genomic language models all you need? Exploring genomic language models on protein downstream tasks.

Large language models, trained on enormous corpora of biological sequences, are state-of-the-art for downstream genomic and proteomic tasks. Since the genome contains the information to encode all proteins, genomic language models (gLMs) hold the potential to make downstream predictions not only about DNA sequences, but also about proteins. However, the performance of gLMs on protein tasks remains unknown, due to few tasks pairing proteins with the coding DNA sequences (CDS) that can be processed by gLMs. In this work, we curated five such datasets and used them to evaluate the performance of gLMs and proteomic language models (pLMs). We show that gLMs are competitive and even outperform their pLMs counterparts on some tasks. The best performance was achieved using the retrieved CDS compared to sampling strategies. We found that training a joint genomic-proteomic model outperforms each individual approach, showing that they capture different but complementary sequence representations, as we demonstrate through model interpretation of their embeddings. Lastly, we explored different genomic tokenization schemes to improve downstream protein performance. We trained a new Nucleotide Transformer (50M) foundation model with 3mer tokenization that outperforms its 6mer counterpart on protein tasks while maintaining performance on genomics tasks. The application of gLMs to proteomics offers the potential to leverage rich CDS data, and in the spirit of the central dogma, the possibility of a unified and synergistic approach to genomics and proteomics. We make our inference code, 3mer pre-trained model weights and datasets available.

Noise robustness and metabolic load determine the principles of central dogma regulation.

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Noise robustness and metabolic load determine the principles of central dogma regulation.

Fitness maximization predicts protein overabundance, a transcriptional floor, and the balancing of transcription and translation.

Deregulation of mitochondrial gene expression in cancer: mechanisms and therapeutic opportunities.

"Reprogramming of energy metabolism" was first considered an emerging hallmark of cancer in 2011 by Hanahan & Weinberg and is now considered a core hallmark of cancer. Mitochondria are the hubs of metabolism, crucial for energetic functions and cellular homeostasis. The mitochondrion's bacterial origin and preservation of their own genome, which encodes proteins and RNAs essential to their function, make them unique organelles. Successful generation of mitochondrial gene products requires coordinated functioning of the mitochondrial 'central dogma,' encompassing all steps necessary for mtDNA to yield mitochondrial proteins. Each of these processes has several levels of regulation, including mtDNA accessibility and protection through mtDNA packaging and epigenetic modifications, mtDNA copy number through mitochondrial replication, mitochondrial transcription through mitochondrial transcription factors, and mitochondrial translation through mitoribosome formation. Deregulation of these mitochondrial processes in the context of cancers has only recently been appreciated, with most studies being correlative in nature. Nonetheless, numerous significant associations of the mitochondrial central dogma with pro-tumor phenotypes have been documented. Several studies have even provided mechanistic insights and further demonstrated successful pharmacologic targeting strategies. Based on the emergent importance of mitochondria for cancer biology and therapeutics, it is becoming increasingly important that we gain an understanding of the underpinning mechanisms so they can be successfully therapeutically targeted. It is expected that this mechanistic understanding will result in mitochondria-targeting approaches that balance anticancer potency with normal cell toxicity. This review will focus on current evidence for the dysregulation of mitochondrial gene expression in cancers, as well as therapeutic opportunities on the horizon.

Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.

Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled "Functional impact of glycans and their curation" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

Characterization of Intrinsically Disordered Proteins in Healthy and Diseased States by Nuclear Magnetic Resonance.

Intrinsically Disordered Proteins (IDPs) are active in different cellular procedures like ordered assembly of chromatin and ribosomes, interaction with membrane, protein, and ligand binding, molecular recognition, binding, and transportation via nuclear pores, microfilaments and microtubules process and disassembly, protein functions, RNA chaperone, and nucleic acid binding, modulation of the central dogma, cell cycle, and other cellular activities, post-translational qualification and substitute splicing, and flexible entropic linker and management of signaling pathways. The intrinsic disorder is a precise structural characteristic that permits IDPs/IDPRs to be involved in both one-to-many and many-to-one signaling. IDPs/IDPRs also exert some dynamical and structural ordering, being much less constrained in their activities than folded proteins. Nuclear magnetic resonance (NMR) spectroscopy is a major technique for the characterization of IDPs, and it can be used for dynamic and structural studies of IDPs. This review was carried out to discuss intrinsically disordered proteins and their different goals, as well as the importance and effectiveness of NMR in characterizing intrinsically disordered proteins in healthy and diseased states.

An ancestral fold reveals the evolutionary link between RNA polymerase and ribosomal proteins

Numerous molecular machines are required to drive the central dogma of molecular biology. However, the means by which these numerous proteins emerged in the early evolutionary stage of life remains enigmatic. Many of them possess small β-barrel folds with different topologies, represented by double-psi β-barrels (DPBBs) conserved in DNA and RNA polymerases, and similar but topologically distinct six-stranded β-barrel RIFT or five-stranded β-barrel folds such as OB and SH3 in ribosomal proteins. Here, we discover that the previously reconstructed ancient DPBB sequence could also adopt a β-barrel fold named Double-Zeta β-barrel (DZBB), as a metamorphic protein. The DZBB fold is not found in any modern protein, although its structure shares similarities with RIFT and OB. Indeed, DZBB could be transformed into them through simple engineering experiments. Furthermore, the OB designs could be further converted into SH3 by circular-permutation as previously predicted. These results indicate that these β-barrels diversified quickly from a common ancestor at the beginning of the central dogma evolution.

Protocell Effects on RNA Folding, Function, and Evolution.

ConspectusCreating a living system from nonliving matter is a great challenge in chemistry and biophysics. The early history of life can provide inspiration from the idea of the prebiotic "RNA World" established by ribozymes, in which all genetic and catalytic activities were executed by RNA. Such a system could be much simpler than the interdependent central dogma characterizing life today. At the same time, cooperative systems require a mechanism such as cellular compartmentalization in order to survive and evolve. Minimal cells might therefore consist of simple vesicles enclosing a prebiotic RNA metabolism.The internal volume of a vesicle is a distinctive environment due to its closed boundary, which alters diffusion and available volume for macromolecules and changes effective molecular concentrations, among other considerations. These physical effects are mechanistically distinct from chemical interactions, such as electrostatic repulsion, that might also occur between the membrane boundary and encapsulated contents. Both indirect and direct interactions between the membrane and RNA can give rise to nonintuitive, "emergent" behaviors in the model protocell system. We have been examining how encapsulation inside membrane vesicles would affect the folding and activity of entrapped RNA.Using biophysical techniques such as FRET, we characterized ribozyme folding and activity inside vesicles. Encapsulation inside model protocells generally promoted RNA folding, consistent with an excluded volume effect, independently of chemical interactions. This energetic stabilization translated into increased ribozyme activity in two different systems that were studied (hairpin ribozyme and self-aminoacylating RNAs). A particularly intriguing finding was that encapsulation could rescue the activity of mutant ribozymes, suggesting that encapsulation could affect not only folding and activity but also evolution. To study this further, we developed a high-throughput sequencing assay to measure the aminoacylation kinetics of many thousands of ribozyme variants in parallel. The results revealed an unexpected tendency for encapsulation to improve the better ribozyme variants more than worse variants. During evolution, this effect would create a tilted playing field, so to speak, that would give additional fitness gains to already-high-activity variants. According to Fisher's Fundamental Theorem of Natural Selection, the increased variance in fitness should manifest as faster evolutionary adaptation. This prediction was borne out experimentally during in vitro evolution, where we observed that the initially diverse ribozyme population converged more quickly to the most active sequences when they were encapsulated inside vesicles.The studies in this Account have expanded our understanding of emergent protocell behavior, by showing how simply entrapping an RNA inside a vesicle, which could occur spontaneously during vesicle formation, might profoundly affect the evolutionary landscape of the RNA. Because of the exponential dynamics of replication and selection, even small changes to activity and function could lead to major evolutionary consequences. By closely studying the details of minimal yet surprisingly complex protocells, we might one day trace a pathway from encapsulated RNA to a living system.

Nanopore tweezers show fractional-nucleotide translocation in sequence-dependent pausing by RNA polymerase

RNA polymerases (RNAPs) carry out the first step in the central dogma of molecular biology by transcribing DNA into RNA. Despite their importance, much about how RNAPs work remains unclear, in part because the small (3.4 Angstrom) and fast (~40 ms/nt) steps during transcription were difficult to resolve. Here, we used high-resolution nanopore tweezers to observe the motion of single Escherichia coli RNAP molecules as it transcribes DNA ~1,000 times improved temporal resolution, resolving single-nucleotide and fractional-nucleotide steps of individual RNAPs at saturating nucleoside triphosphate concentrations. We analyzed RNAP during processive transcription elongation and sequence-dependent pausing at the yrbL elemental pause sequence. Each time RNAP encounters the yrbL elemental pause sequence, it rapidly interconverts between five translocational states, residing predominantly in a half-translocated state. The kinetics and force-dependence of this half-translocated state indicate it is a functional intermediate between pre- and post-translocated states. Using structural and kinetics data, we show that, in the half-translocated and post-translocated states, sequence-specific protein-DNA interaction occurs between RNAP and a guanine base at the downstream end of the transcription bubble (core recognition element). Kinetic data show that this interaction stabilizes the half-translocated and post-translocated states relative to the pre-translocated state. We develop a kinetic model for RNAP at the yrbL pause and discuss this in the context of key structural features.

Excited-Ground-State Transition of the RNA Strand Slippage Mechanism Captured by the Base-Specific Force Field.

Excited-ground-state transition and strand slippage of RNA play key roles in transcription and translation of central dogma. Due to limitation of current experimental techniques, the dynamic structure ensembles of RNA remain inadequately understood. Molecular dynamics simulations offer a promising complementary approach, whose accuracy depends on the force field. Here, we develop the new version of RNA base-specific force field (BSFF2) to address underestimation of base pairing stability and artificial backbone conformations. Extensive evaluations on typical RNA systems have comprehensively confirmed the accuracy of BSFF2. Furthermore, BSFF2 demonstrates exceptional efficiency in de novo folding of tetraloops and reproducing base pair reshuffling transition between RNA excited and ground states. Then, we explored the RNA strand slippage mechanism with BSFF2. We conducted a comprehensive three-dimensional structural investigation into the strand slippage of the most complex r(G4C2)9 repeat element and presented the molecular details in the dynamic transition along with the underlying mechanism. Our results of capturing the strand slippage, excited-ground transition, de novo folding, and simulations for various typical RNA motifs indicate that BSFF2 should be one of valuable tools for dynamic conformation research and structure prediction of RNA, and a future contribution to RNA-targeted drug design as well as RNA therapy development.