60 years ago, Francis Crick changed the logic of biology

Non-coding RNAs, a double-edged sword in breast cancer prognosis

Cancer is a rising issue worldwide, and numerous studies have focused on understanding the underlying reasons for its occurrence and finding proper ways to defeat it. By applying technological advances, researchers are continuously uncovering and updating treatments in cancer therapy. Their vast functions in the regulation of cell growth and proliferation and their significant role in the progression of diseases, including cancer. This review provides a comprehensive analysis of ncRNAs in breast cancer, focusing on long non-coding RNAs such as HOTAIR, MALAT1, and NEAT1, as well as microRNAs such as miR-21, miR-221/222, and miR-155. These ncRNAs are pivotal in regulating cell proliferation, metastasis, drug resistance, and apoptosis. Additionally, we discuss experimental approaches that are useful for studying them and highlight the advantages and challenges of each method. We then explain the results of these clinical trials and offer insights for future studies by discussing major existing gaps. On the basis of an extensive number of studies, this review provides valuable insights into the potential of ncRNAs in cancer therapy. Key findings show that even though the functions of ncRNAs are vast and undeniable in cancer, there are still complications associated with their therapeutic use. Moreover, there is an absence of sufficient experiments regarding their application in mouse models, which is an area to work on. By emphasizing the crucial role of ncRNAs, this review underscores the need for innovative approaches and further studies to explore their potential in cancer therapy.

Network analysis reveals protein modules associated with childhood respiratory diseases

BACKGROUND

The first year of life represents a dynamic immune development period that impacts the risk of developing respiratory-related diseases, including asthma, recurrent infections, and eczema. However, the role of immune-mediating proteins in childhood respiratory diseases is not well characterized in early life.

OBJECTIVE

The objective of this study was to investigate relationships between protein profiles at age 1 year and respiratory-related diseases by age 6 years, including asthma, recurrent wheeze, respiratory infections, and eczema.

METHODS

We applied weighted gene correlation network analysis to derive modules of highly correlated proteins during early life immune development using plasma samples collected from children at age 1 year (n = 294) in the Vitamin D Antenatal Asthma Reduction Trial. Using regression analysis, we evaluated relationships between protein modules at age 1 and respiratory-related diseases by age 6. We integrated protein modules with additional omics and social, demographic, and environmental data for further characterization.

RESULTS

Our analysis identified 4 protein modules at age 1 year associated with incidence of childhood asthma and/or recurrent wheeze (adjusted Ps = .02 to .03), respiratory infections (adjusted Ps = 6.3 × 10-9 to 2.9 × 10-6), and eczema (adjusted P = .01) by age 6 years; associations between modules and clinical outcomes were temporally sensitive and were not recapitulated using protein profiles at age 6 years. Age 1 modules were associated with environmental factors (adjusted Ps = 2.8 × 10-10 to .03) and alterations in metabolomic pathways (adjusted Ps = 2.8 × 10-6 to .04). No genome-wide single nucleotide polymorphisms were identified for any protein module.

CONCLUSION

These findings suggested that protein profiles at age 1 year predicted development of respiratory-related diseases by age 6. Applying network approaches to study protein profiles may represent a new strategy to identify children susceptible to respiratory-related diseases in the first year of life.

STForte: tissue context-specific encoding and consistency-aware spatial imputation for spatially resolved transcriptomics

Encoding spatially resolved transcriptomics (SRT) data serves to identify the biological semantics of RNA expression within the tissue while preserving spatial characteristics. Depending on the analytical scenario, one may focus on different contextual structures of tissues. For instance, anatomical regions reveal consistent patterns by focusing on spatial homogeneity, while elucidating complex tumor micro-environments requires more expression heterogeneity. However, current spatial encoding methods lack consideration of the tissue context. Meanwhile, most developed SRT technologies are still limited in providing exact patterns of intact tissues due to limitations such as low resolution or missed measurements. Here, we propose STForte, a novel pairwise graph autoencoder-based approach with cross-reconstruction and adversarial distribution matching, to model the spatial homogeneity and expression heterogeneity of SRT data. STForte extracts interpretable latent encodings, enabling downstream analysis by accurately portraying various tissue contexts. Moreover, STForte allows spatial imputation using only spatial consistency to restore the biological patterns of unobserved locations or low-quality cells, thereby providing fine-grained views to enhance the SRT analysis. Extensive evaluations of datasets under different scenarios and SRT platforms demonstrate that STForte is a scalable and versatile tool for providing enhanced insights into spatial data analysis.

Prediction of Lung Adenocarcinoma Driver Genes Through Protein-Protein Interaction Networks Utilizing GenePlexus

Lung adenocarcinoma, a subtype of lung cancer, is produced by uncontrolled proliferation of somatic cells affected by some tumorigenic factors. The origin of this disease can be attributed to the concept of "cancer driver," which links the occurrence of tumor with specific changes in some key genes. These key genes can be identified at various molecular levels. Our innovative method uses a groundbreaking computing technology called GenePlexus to mine new genes related to lung adenocarcinoma. Initially, a vast network was synthesized from protein-protein interactions. Utilizing GenePlexus, we traversed paths interlinking aberrant genes across different layers and pinpointed emerging candidate genes situated on these trajectories. Finally, the candidate genes that were obtained underwent a series of filtering processes, including a permutation test, interaction test, and enrichment test. Compared with the shortest path method, GenePlexus has identified previously neglected genes involved in lung adenocarcinoma. For example, genes such as EGR2, EPHA3, FGFR4, HOXB1, and HEY1 play key roles at multiple molecular levels, including methylation, microRNA, mRNA and mutation, which affect tumorigenesis and lung cancer progression. These genes regulate various processes, from gene expression and cell proliferation to drug resistance to therapeutic drugs and the progress of lung adenocarcinoma.

Cap-specific terminal N6-methyladeonsine methylation of RNA mediated by PCIF1 and possible therapeutic implications

Posttranscriptional RNA modification is an important mode of epigenetic regulation in various biological and pathological contexts. N6, 2'-O-dimethyladenosine (m6Am) is one of the most abundant methylation modifications in mammals and usually occurs at the first transcribed nucleotide. Accumulating evidence indicates that m6Am modifications have important roles in RNA metabolism and physiological and pathological processes. PCIF1 (phosphorylated C-terminal domain interacting factor 1) is a protein that can bind to the phosphorylated C-terminal domain of RNA polymerase II through its WW domain. PCIF1 is named after this binding ability. Recently, PCIF1 has been identified as a cap-specific adenine N6-methyltransferase responsible for m6Am formation. Discovered as the sole m6Am methyltransferase for mammalian mRNA, PCIF1 has since received more extensive and in-depth study. Dysregulation of PCIF1 contributes to various pathological processes. Targeting PCIF1 may hold promising therapeutic significance. In this review, we provide an overview of the current knowledge of PCIF1. We explore the current understanding of the structure and the biological characteristics of PCIF1. We further review the molecular mechanisms of PCIF1 in cancer and viral infection and discuss its therapeutic potential.

Lasso peptides realm: Insights and applications

Lasso peptides exhibit a range of bioactivities, including antiviral effects, inhibition of the glucagon receptor, blockade of the endothelin type B receptor, inhibition of myosin light chain kinase, and modulation of the atrial natriuretic factor, as well as notable antimicrobial properties. Intriguingly, lasso peptides exhibit remarkable proteolytic and thermal stability, addressing one of the key challenges that traditional peptides often face. The challenge in producing those valuable peptides remains the main hurdle in the way of producing larger quantities or even modifying them with more potent analogues. Genome mining and heterologous expression approaches have greatly facilitated the production of lasso peptides, moving beyond mere isolation techniques. This advancement not only allows for larger quantities but also enables the creation of additional analogues with improved stability and potency. This review aims to explore the unique bioactivities and stability of lasso peptides, along with recent advancements in genome mining and heterologous expression that address production challenges and open pathways for engineering potent analogues.

From straight to curved: A historical perspective of DNA shape

DNA is the macromolecule responsible for storing the genetic information of a cell and it has intrinsic properties such as deformability, stability and curvature. DNA Curvature plays an important role in gene transcription and, consequently, in the subsequent production of proteins, a fundamental process of cells. With recent advances in bioinformatics and theoretical biology, it became possible to analyze and understand the involvement of DNA Curvature as a discriminatory characteristic of gene-promoting regions. These regions act as sites where RNAp (ribonucleic acid-polymerase) binds to initiate transcription. This review aims to describe the formation of Curvature, as well as highlight its importance in predicting promoters. Furthermore, this article provides the potential of DNA Curvature as a distinguishing feature for promoter prediction tools, as well as outlining the calculation procedures that have been described by other researchers. This work may support further studies directed towards the enhancement of promoter prediction software.

Selective Effect of DNA N6-Methyladenosine Modification on Transcriptional Genetic Variations in East Asian Samples

Genetic variations and DNA modification are two common dominant factors ubiquitous across the entire human genome and induce human disease, especially through static genetic variations in DNA or RNA that cause human genetic diseases. DNA N6-methyladenosine (6mA) methylation, as a new epigenetic modification mark, has been widely studied for regulatory biological processes in humans. However, the effect of DNA modification on dynamic transcriptional genetic variations from DNA to RNA has rarely been reported. Here, we identified DNA, RNA and transcriptional genetic variations from Illumina short-read sequencing data in East Asian samples (HX1 and AK1) and detected global DNA 6mA modification using single-molecule, real-time sequencing (SMRT) data. We decoded the effects of DNA 6mA modification on transcriptional genetic variations in East Asian samples and the results were extensively verified in the HeLa cell line. DNA 6mA modification had a stabilized distribution in the East Asian samples and the methylated genes were less likely to mutate than the non-methylated genes. For methylated genes, the 6mA density was positively correlated with the number of variations. DNA 6mA modification had a selective effect on transcriptional genetic variations from DNA to RNA, in which the dynamic transcriptional variations of heterozygous (0/1 to 0/1) and homozygous (1/1 to 1/1) were significantly affected by 6mA modification. The effect of DNA methylation on transcriptional genetic variations provides new insights into the influencing factors of DNA to RNA transcriptional regulation in the central doctrine of molecular biology.

RNA-Binding Small Molecules in Drug Discovery and Delivery: An Overview from Fundamentals

RNA molecules, similar to proteins, fold into complex structures to confer diverse functions in cells. The intertwining of functions with RNA structures offers a new therapeutic opportunity for small molecules to bind and manipulate disease-relevant RNA pathways, thus creating a therapeutic realm of RNA-binding small molecules. The ongoing interest in RNA targeting and subsequent screening campaigns have led to the identification of numerous compounds that can regulate RNAs from splicing, degradation to malfunctions, with therapeutic benefits for a variety of diseases. Moreover, along with the rise of RNA-based therapeutics, RNA-binding small molecules have expanded their application to the modification, regulation, and delivery of RNA drugs, leading to the burgeoning interest in this field. This Perspective overviews the emerging roles of RNA-binding small molecules in drug discovery and delivery, covering aspects from their action fundamentals to therapeutic applications, which may inspire researchers to advance the field.

Pioneering role of RNA in the early evolution of life

The catalytic, regulatory and structural properties of RNA, combined with their extraordinary ubiquity in cellular processes, are consistent with the proposal that this molecule played a much more conspicuous role in heredity and metabolism during the early stages of biological evolution. This review explores the pivotal role of RNA in the earliest life forms and its relevance in modern biological systems. It examines current models that study the early evolution of life, providing insights into the primordial RNA world and its legacy in contemporary biology.

A comprehensive adsorption and desorption study on the interaction of DNA oligonucleotides with TiO2 nanolayers

The utilization of TiO2 nanolayers that possess excellent biocompatibility and physical properties in DNA sensing and sequencing remains largely to be explored. To examine their applicability in gene sequencing, a comprehensive study on the interaction of DNA oligonucleotides with TiO2 nanolayers was performed through adsorption and desorption experiments. TiO2 nanolayers with 10 nm thickness were fabricated via magnetron sputtering onto a 6-inch silicon wafer. A simple chip block method, validated via quartz crystal microbalance experiments with dissipation monitoring (QCM-D), was proposed to study the adsorption behaviors and interaction mechanisms under a variety of critical influencing factors, including DNA concentration, length, and type, adsorption time, pH, and metal ions. It is determined that the adsorption takes 2 h to reach saturation in the MES solution and the adsorption capacity is significantly enhanced by lowering the pH due to the isoelectric point being pH = 6 for TiO2. The adsorption percentages of nucleobases are largely similar in the MES solution while following 5T = 5G > 5C > 5A in HEPES buffer for an adsorption duration of 2.5 h. Through pre-adsorption experiments, it is deduced that DNA oligonucleotides are horizontally adsorbed on the nanolayer. This further demonstrates that mono-, di-, and tri-valent metal ions promote the adsorption, whereas Zn2+ has strong adsorption by inducing DNA condensation. Based on the desorption experiments, it is revealed that electrostatic force dominates the adsorption over van der Waals force and hydrogen bonds. The phosphate group is the main functional group for adsorption, and the adsorption strength increases with the length of the oligonucleotide. This study provides comprehensive data on the adsorption of DNA oligonucleotides onto TiO2 nanolayers and clarifies the interaction mechanisms therein, which will be valuable for applications of TiO2 in DNA-related applications.

Multiple Protein Profiler 1.0 (MPP): A Webserver for Predicting and Visualizing Physiochemical Properties of Proteins at the Proteome Level

Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/ .

The Origin of RNA and the Formose-Ribose-RNA Pathway

Prebiotic pre-Darwinian reactions continued throughout biochemical or Darwinian evolution. Early chemical processes could have occurred on Earth between 4.5 and 3.6 billion years ago when cellular life was about to come into being. Pre-Darwinian evolution assumes the development of hereditary elements but does not regard them as self-organizing processes. The presence of biochemical self-organization after the pre-Darwinian evolution did not justify distinguishing between different types of evolution. From the many possible solutions, evolution selected from among those stable reactions that led to catalytic networks, and under gradually changing external conditions produced a reproducible, yet constantly evolving and adaptable, living system. Major abiotic factors included sunlight, precipitation, air, minerals, soil and the Earth's atmosphere, hydrosphere and lithosphere. Abiotic sources of chemicals contributed to the formation of prebiotic RNA, the development of genetic RNA, the RNA World and the initial life forms on Earth and the transition of genRNA to the DNA Empire, and eventually to the multitude of life forms today. The transition from the RNA World to the DNA Empire generated new processes such as oxygenic photosynthesis and the hierarchical arrangement of processes involved in the transfer of genetic information. The objective of this work is to unite earlier work dealing with the formose, the origin and synthesis of ribose and RNA reactions that were published as a series of independent reactions. These reactions are now regarded as the first metabolic pathway.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

Crosstalk between histone/DNA modifications and RNA N6-methyladenosine modification

N6-methyladenosine (m6A) is the most prevalent internal RNA modification in eukaryotic messenger RNAs (mRNAs), regulating gene expression at the transcription and post-transcription levels. Complex interplay between m6A and other well-studied epigenetic modifications, including histone modifications and DNA modification, has been extensively reported in recent years. The crosstalk between RNA m6A modification and histone/DNA modifications plays a critical role in establishing the chromatin state for the precise and specific fine-tuning of gene expression and undoubtedly has profound impacts on both physiological and pathological processes. In this review, we discuss the crosstalk between RNA m6A modification and histone/DNA modifications, emphasizing their sophisticated communications and the mechanisms underlying to gain a comprehensive view of the biological relevance of m6A-based epigenetic network.

Niclosamide: A career builder

My contribution to honoring Professor Kinam Park celebrates and resonates with his scholarly career in drug delivery, his commitment to encouraging the next generation(s), and his efforts to keep us focused on clinically effective formulations. To do this I take as my example, niclosamide, a small molecule protonophore that, uniquely, can "target" all cell membranes, both plasma and organelle. As such, it acts upstream of many cell pathways and so has the potential to affect many of the essential events that a cell, and particularly a diseased cell or other entities like a virus, use to stay alive and prosper. Literature shows that it has so far been discovered to positively influence (at least): cancer, bacterial and viral infection, metabolic diseases such as Type II diabetes, NASH and NAFLD, artery constriction, endometriosis, neuropathic pain, rheumatoid arthritis, sclerodermatous graft-versus-host disease, systemic sclerosis, Parkinson's, and COPD. With such a fundamental action and broad-spectrum activity, I believe that studying niclosamide in all its manifestations, discovering if and to what extent it can contribute positively to disease control (and also where it can't), formulating it as effective therapeutics, and testing them in preclinical and clinical trials is a career builder for our next generation(s). The article is divided into two parts: Part I introduces niclosamide and other proton shunts mainly in cancer and viral infections and reviews an exponentially growing literature with some concepts and physicochemical properties that lead to its proton shunt mechanism. Part II focuses on repurposing by reformulation of niclosamide. I give two examples of "carrier-free formulations", - one for cancer (as a prodrug therapeutic of niclosamide stearate for i.v. and other administration routes, exemplified by our recent work on Osteosarcoma in mice and canine patients), and the other as a niclosamide solution formulation (that could provide the basis for a preventative nasal spray and early treatment option for COVID19 and other respiratory virus infections). My goal is to excite and enthuse, encourage, and motivate all involved in the drug development and testing process in academia, institutes, and industry, to learn more about this interesting molecule and others like it. To enable such endeavors, I give many proposed ideas throughout the document, that have been stimulated and inspired by gaps in the literature, urgent needs in disease, and new studies arising from our own work. The hope is that, by reading through this document and studying the suggested topics and references, the drug delivery and development community will continue our lineage and benefit from our legacy to achieve niclosamide's potential as an effective contributor to the treatment and control of many diseases and conditions.

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

LncRNAs: the good, the bad, and the unknown

Long non-coding RNAs (lncRNAs) are significant contributors in maintaining genomic integrity through epigenetic regulation. LncRNAs can interact with chromatin-modifying complexes in both cis and trans pathways, drawing them to specific genomic loci and influencing gene expression via DNA methylation, histone modifications, and chromatin remodeling. They can also operate as building blocks to assemble different chromatin-modifying components, facilitating their interactions and gene regulatory functions. Deregulation of these molecules has been associated with various human diseases, including cancer, cardiovascular disease, and neurological disorders. Thus, lncRNAs are implicated as potential diagnostic indicators and therapeutic targets. This review discusses the current understanding of how lncRNAs mediate epigenetic control, genomic integrity, and their putative functions in disease pathogenesis.

RNA G-quadruplex in functional regulation of noncoding RNA: Challenges and emerging opportunities

G-quadruplexes (G4s) are stable, noncanonical structures formed in guanine (G)-rich sequences of DNA/RNA. G4 structures are reported to play a regulatory role in various cellular processes and, recently, a considerable number of studies have attributed new biological functions to these structures, especially in RNA. Noncoding RNA (ncRNA), which does not translate into a functional protein, is widely expressed and has been shown to play a key role in shaping cellular activity. There has been growing evidence of G4 formation in several ncRNA classes, and it has been identified as a key part for diverse biological functions and physio-pathological contexts in neurodegenerative diseases and cancer. This review discusses RNA G4s (rG4s) in ncRNA, focusing on the molecular mechanism underlying its function. This review also aims to highlight potential and emerging opportunities to identify and target the rG4s in ncRNA to understand its function and, ultimately, treat many diseases.

DNA Vaccines: Their Formulations, Engineering and Delivery

The concept of DNA vaccination was introduced in the early 1990s. Since then, advancements in the augmentation of the immunogenicity of DNA vaccines have brought this technology to the market, especially in veterinary medicine, to prevent many diseases. Along with the successful COVID mRNA vaccines, the first DNA vaccine for human use, the Indian ZyCovD vaccine against SARS-CoV-2, was approved in 2021. In the current review, we first give an overview of the DNA vaccine focusing on the science, including adjuvants and delivery methods. We then cover some of the emerging science in the field of DNA vaccines, notably efforts to optimize delivery systems, better engineer delivery apparatuses, identify optimal delivery sites, personalize cancer immunotherapy through DNA vaccination, enhance adjuvant science through gene adjuvants, enhance off-target and heritable immunity through epigenetic modification, and predict epitopes with bioinformatic approaches. We also discuss the major limitations of DNA vaccines and we aim to address many theoretical concerns.

MicroRNAs Associated with a Bad Prognosis in Acute Myeloid Leukemia and Their Impact on Macrophage Polarization

MicroRNAs (miRNAs) are short, non-coding ribonucleic acids (RNAs) associated with gene expression regulation. Since the discovery of the first miRNA in 1993, thousands of miRNAs have been studied and they have been associated not only with physiological processes, but also with various diseases such as cancer and inflammatory conditions. MiRNAs have proven to be not only significant biomarkers but also an interesting therapeutic target in various diseases, including cancer. In acute myeloid leukemia (AML), miRNAs have been regarded as a welcome addition to the limited therapeutic armamentarium, and there is a vast amount of data on miRNAs and their dysregulation. Macrophages are innate immune cells, present in various tissues involved in both tissue repair and phagocytosis. Based on their polarization, macrophages can be classified into two groups: M1 macrophages with pro-inflammatory functions and M2 macrophages with an anti-inflammatory action. In cancer, M2 macrophages are associated with tumor evasion, metastasis, and a poor outcome. Several miRNAs have been associated with a poor prognosis in AML and with either the M1 or M2 macrophage phenotype. In the present paper, we review miRNAs with a reported negative prognostic significance in cancer with a focus on AML and analyze their potential impact on macrophage polarization.

Biologicalization of Smart Manufacturing Using DNA-Based Computing

Smart manufacturing needs cognitive computing methods to make the relevant systems more intelligent and autonomous. In this respect, bio-inspired cognitive computing methods (i.e., biologicalization) can play a vital role. This article is written from this perspective. In particular, this article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it cannot get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, but not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like). This characteristic of DBC can help to solve cognitive problems (e.g., pattern recognition). DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, we demonstrate that DBC-1 can solve various cognitive problems, e.g., "similarity indexing between seemingly different but inherently identical objects" and "recognizing regions of an image separated by a complex boundary." In addition, using an arbitrary numerical example, we demonstrate that DBC-2 can solve the following cognitive problem: "pattern recognition when the relevant information is insufficient." The remarkable thing is that smart manufacturing-based systems (e.g., digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (e.g., machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and enrich their biologicalization.

ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion

MOTIVATION

The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithful transfer of genetic data from generation to generation. The advent of experimental techniques has aided in the discovery of ORIs in many species. Experimentation, on the other hand, is often more time-consuming and pricey than computational approaches, and it necessitates specific equipment and knowledge. Recently, ORI sites have been predicted using computational techniques like motif-based searches and artificial intelligence algorithms based on sequence characteristics and chromatin states.

RESULTS

In this article, we developed ORI-Explorer, a unique artificial intelligence-based technique that combines multiple feature engineering techniques to train CatBoost Classifier for recognizing ORIs from four distinct eukaryotic species. ORI-Explorer was created by utilizing a unique combination of three traditional feature-encoding techniques and a feature set obtained from a deep-learning neural network model. The ORI-Explorer has significantly outperformed current predictors on the testing dataset. Furthermore, by employing the sophisticated SHapley Additive exPlanation method, we give crucial insights that aid in comprehending model success, highlighting the most relevant features vital for forecasting cell-specific ORIs. ORI-Explorer is also intended to aid community-wide attempts in discovering potential ORIs and developing innovative verifiable biological hypotheses.

AVAILABILITY AND IMPLEMENTATION

The used datasets along with the source code are made available through https://github.com/Z-Abbas/ORI-Explorer and https://zenodo.org/record/8358679.

Enhancer dynamics: Unraveling the mechanism of transcriptional bursting

Transcriptional bursting is a prevalent feature of gene expression. The transient assembly of transcription factor clusters at regulatory DNAs is critical to control bursting dynamics.

Nearly maximal information gain due to time integration in central dogma reactions

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we demonstrate that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain because of time integration while also keeping the loss because of stochasticity in translation relatively low (<0.5 bits).

PyKleeBarcode: Enabling representation of the whole animal kingdom in information space

As biological sequence databases continue growing, so do the insight that they promise to shed on the shape of the genetic diversity of life. However, to fulfil this promise the software must remain usable, be able to accommodate a large amount of data and allow use of modern high performance computing infrastructure. In this study we present a reimplementation as well as an extension of a technique using indicator vectors to compute and visualize similarities between sets of nucleotide sequences. We have a flexible and easy to use python program relying on standard and open-source libraries. Our tool allows analysis of very large complement of sequences using code parallelization, as well as by providing routines to split a computational task in smaller and manageable subtasks whose results are then merged. This implementation also facilitates adding new sequences into an indicator vector-based representation without re-computing the whole set. The efficient synthesis of data into knowledge is no trivial matter given the size and rapid growth of biological sequence databases. Based on previous results regarding the properties of indicator vectors, the open-source approach proposed here efficiently and flexibly supports comparative analysis of genetic diversity at a large scale. Our software is freely available at: https://github.com/WandrilleD/pyKleeBarcode.

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.

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.

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.

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.

Plant translational reprogramming for stress resilience

Organisms regulate gene expression to produce essential proteins for numerous biological processes, from growth and development to stress responses. Transcription and translation are the major processes of gene expression. Plants evolved various transcription factors and transcriptome reprogramming mechanisms to dramatically modulate transcription in response to environmental cues. However, even the genome-wide modulation of a gene's transcripts will not have a meaningful effect if the transcripts are not properly biosynthesized into proteins. Therefore, protein translation must also be carefully controlled. Biotic and abiotic stresses threaten global crop production, and these stresses are seriously deteriorating due to climate change. Several studies have demonstrated improved plant resistance to various stresses through modulation of protein translation regulation, which requires a deep understanding of translational control in response to environmental stresses. Here, we highlight the translation mechanisms modulated by biotic, hypoxia, heat, and drought stresses, which are becoming more serious due to climate change. This review provides a strategy to improve stress tolerance in crops by modulating translational regulation.

De-centralizing the Central Dogma: mRNA translation in space and time

As our understanding of the cell interior has grown, we have come to appreciate that most cellular operations are localized, that is, they occur at discrete and identifiable locations or domains. These cellular domains contain enzymes, machines, and other components necessary to carry out and regulate these localized operations. Here, we review these features of one such operation: the localization and translation of mRNAs within subcellular compartments observed across cell types and organisms. We describe the conceptual advantages and the "ingredients" and mechanisms of local translation. We focus on the nature and features of localized mRNAs, how they travel and get localized, and how this process is regulated. We also evaluate our current understanding of protein synthesis machines (ribosomes) and their cadre of regulatory elements, that is, the translation factors.

Complexity and self-organization in the evolution of cell polarization

Cellular life exhibits order and complexity, which typically increase over the course of evolution. Cell polarization is a well-studied example of an ordering process that breaks the internal symmetry of a cell by establishing a preferential axis. Like many cellular processes, polarization is driven by self-organization, meaning that the macroscopic pattern emerges as a consequence of microscopic molecular interactions at the biophysical level. However, the role of self-organization in the evolution of complex protein networks remains obscure. In this Review, we provide an overview of the evolution of polarization as a self-organizing process, focusing on the model species Saccharomyces cerevisiae and its fungal relatives. Moreover, we use this model system to discuss how self-organization might relate to evolutionary change, offering a shift in perspective on evolution at the microscopic scale.

Elastic Fluorescent Protein-Based Down-Converting Optical Films for Flexible Display

Protein-based material design provides great advantages to developing smart biomaterials with tunable structures and desired functions. They have been widely used in many biomedical applications including tissue engineering and drug delivery. However, protein-based materials are not yet widely used in optoelectronic materials despite their excellent optical and tunable mechanical properties. Here, we synthesized engineered fluorescent proteins (FPs) fused with elastic protein for the development of optoelectrical down-converting optical filters for flexible display materials. We synthesized sequence-specific FPs to tune blue, green, yellow, and red colors and fused them with elastic protein to tune mechanical properties. We fabricated flexible self-supporting film materials and characterized mechanical properties and down-converting optical properties. We also fabricated a hybrid light-emitting diode (LED) to down convert blue to desired green, red, and white colors. Furthermore, we constructed a flexible white LED using organic LED as a flexible substrate. Our modular synthesis approach of tunable bio-optoelectrical material approaches will be useful to design future biocompatible and flexible display materials and technologies.

SERPINA3: Stimulator or Inhibitor of Pathological Changes

SERPINA3, also called α-1-antichymotrypsin (AACT, ACT), is one of the inhibitors of serine proteases, one of which is cathepsin G. As an acute-phase protein secreted into the plasma by liver cells, it plays an important role in the anti-inflammatory response and antiviral response. Elevated levels of SERPINA3 have been observed in heart failure and neurological diseases such as Alzheimer's disease or Creutzfeldt-Jakob disease. Many studies have shown increased expression levels of the SERPINA3 gene in various types of cancer, such as glioblastoma, colorectal cancer, endometrial cancer, breast cancer, or melanoma. In this case, the SERPINA3 protein is associated with an antiapoptotic function implemented by adjusting the PI3K/AKT or MAPK/ERK 1/2 signal pathways. However, the functions of the SERPINA3 protein are still only partially understood, mainly in the context of cancerogenesis, so it seems necessary to summarize the available information and describe its mechanism of action. In particular, we sought to amass the existing body of research focusing on the description of the underlying mechanisms of various diseases not related to cancer. Our goal was to present an overview of the correct function of SERPINA3 as part of the defense system, which unfortunately easily becomes the "Fifth Column" and begins to support processes of destruction.

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Genetically engineered crops for sustainably enhanced food production systems

Genetic modification of crops has substantially focused on improving traits for desirable outcomes. It has resulted in the development of crops with enhanced yields, quality, and tolerance to biotic and abiotic stresses. With the advent of introducing favorable traits into crops, biotechnology has created a path for the involvement of genetically modified (GM) crops into sustainable food production systems. Although these plants heralded a new era of crop production, their widespread adoption faces diverse challenges due to concerns about the environment, human health, and moral issues. Mitigating these concerns with scientific investigations is vital. Hence, the purpose of the present review is to discuss the deployment of GM crops and their effects on sustainable food production systems. It provides a comprehensive overview of the cultivation of GM crops and the issues preventing their widespread adoption, with appropriate strategies to overcome them. This review also presents recent tools for genome editing, with a special focus on the CRISPR/Cas9 platform. An outline of the role of crops developed through CRSIPR/Cas9 in achieving sustainable development goals (SDGs) by 2030 is discussed in detail. Some perspectives on the approval of GM crops are also laid out for the new age of sustainability. The advancement in molecular tools through plant genome editing addresses many of the GM crop issues and facilitates their development without incorporating transgenic modifications. It will allow for a higher acceptance rate of GM crops in sustainable agriculture with rapid approval for commercialization. The current genetic modification of crops forecasts to increase productivity and prosperity in sustainable agricultural practices. The right use of GM crops has the potential to offer more benefit than harm, with its ability to alleviate food crises around the world.

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

The Central Dogma revisited: Insights from protein synthesis, CRISPR, and beyond

Francis Crick advanced two distinct but interrelated fundamental principles of molecular biology: (1) the Sequence Hypothesis and (2) the Central Dogma. The Sequence Hypothesis defines biological information transfer as the residue-by-residue transfer of sequence information between nucleic acids and to proteins. This is commonly summarized as DNA ➔ RNA ➔ protein and is colloquially referred to as the Central Dogma. More specifically, however, the Central Dogma expounded by Crick included a critical restriction, stipulating that "once sequential information has passed into protein it cannot get out again." Under this definition, the Central Dogma has stood the test of time despite challenges. In principle, a violation of the Central Dogma could transpire through synthetic biology or by natural occurrence. To address these possibilities, we draw insights from existing modes of information transfer in protein synthesis and from synthetic Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR) gene-editing. We introduce a three-part evaluation scheme, which we apply to the CRISPR/Cas9 system and the more recent CRISPR prime editing system. Potential mechanisms by which engineered sequence editing systems might violate the Central Dogma are considered. We conclude that although information transfer in protein synthesis and CRISPR gene-editing remain within the bounds of the Central Dogma, the underlying mechanisms point toward an avenue of synthetic biology that could directly violate the Central Dogma. Finally, we speculate on some of the theoretical and practical implications of a protein-derived information transfer system. This article is categorized under: RNA Evolution and Genomics > Ribonomics RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Mechanisms.

The Central Dogma of Information

Info-autopoiesis or the self-referenced, recursive, interactive process of information self-production that engages all living beings in their efforts to satisfy their physiological and/or relational needs relies on Bateson’s difference which makes a difference. Living beings, as active manipulators/observers of their environment, derive meaning from the sensorially detected motion of matter and/or energy in the Universe. The process of info-autopoiesis in humans is found to be triadic in nature and incorporates the simultaneity of a quantitative/objective perspective with a qualitative/subjective perspective. In this process of meaningful engagement with the environment, humans create and transform endogenous semantic information into countless expressions of exogeneous syntactic information, which is synonymous with ordered material structure and artificial creation. Other humans can interpret exogeneous syntactic information and uniquely transform it into semantic information that can take multifarious forms. This asymmetrical process is the basis to postulate the central dogma of information that states ‘info-autopoiesis results in endogenous semantic information that irreversibly becomes exogeneous syntactic information’. In other words, once the artificial, syntactic world, including machines, created by humans comes into being it can only be interpreted by others, i.e., it does not necessarily convey the same intended meaning to all. Additionally, these artificial creations only recognize, extract, create, transmit, preserve, store, and utilize syntactic information, unable to transform syntactic information into semantic information. In other words, our resourceful capacity for syntactic creation does not allow for creation of artificial beings with comparable capabilities as us for meaning making. It suggests that our dreams for sentient artificial general intelligence and superintelligence are misguided and parallel the central dogma of molecular biology which states that ‘once (sequential) information has passed into protein it cannot get out again’.

Emerging Roles and Potential Applications of Non-Coding RNAs in Cervical Cancer

Cervical cancer (CC) is a preventable disease using proven interventions, specifically prophylactic vaccination, pervasive disease screening, and treatment, but it is still the most frequently diagnosed cancer in women worldwide. Patients with advanced or metastatic CC have a very dismal prognosis and current therapeutic options are very limited. Therefore, understanding the mechanism of metastasis and discovering new therapeutic targets are crucial. New sequencing tools have given a full visualization of the human transcriptome's composition. Non-coding RNAs (NcRNAs) perform various functions in transcriptional, translational, and post-translational processes through their interactions with proteins, RNA, and even DNA. It has been suggested that ncRNAs act as key regulators of a variety of biological processes, with their expression being tightly controlled under physiological settings. In recent years, and notably in the past decade, significant effort has been made to examine the role of ncRNAs in a variety of human diseases, including cancer. Therefore, shedding light on the functions of ncRNA will aid in our better understanding of CC. In this review, we summarize the emerging roles of ncRNAs in progression, metastasis, therapeutics, chemo-resistance, human papillomavirus (HPV) regulation, metabolic reprogramming, diagnosis, and as a prognostic biomarker of CC. We also discussed the role of ncRNA in the tumor microenvironment and tumor immunology, including cancer stem cells (CSCs) in CC. We also address contemporary technologies such as antisense oligonucleotides, CRISPR-Cas9, and exosomes, as well as their potential applications in targeting ncRNAs to manage CC.

Analysing bio-art’s epistemic landscape: from metaphoric to post-metaphoric structure

Since its emergence, bio-art has developed numerous metaphors central to the transfer of concepts of modern biology, genetics, and genomics to the public domain that reveal several cultural, ethical, and social variations in their related themes. This article assumes that a general typology of metaphors developed by practices related to bio-art can be categorised into two categories: pictorial and operational metaphors. Through these, information regarding several biological issues is transferred to the public arena. Based on the analysis, this article attempts to answer the following questions: How does bio-art develop metaphors to advance epistemic and discursive agendas that constitute public understanding of a set of deeply problematic assumptions regarding how today’s biology operates? Under the influence of today’s synthetic biology, could bio-media operationally reframe these epistemic agendas by reframing complex and multi-layered metaphors towards post-metaphoric structures? Finally, what are the scientific, cultural, and social implications of reframing?

ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network

Replication of DNA is an important process for the cell division cycle, gene expression regulation and other biological evolution processes. It also has a crucial role in a living organism's physical growth and structure. Replication of DNA comprises of three stages known as initiation, elongation and termination, whereas the origin of replication sites (ORI) is the location of initiation of the DNA replication process. There exist various methodologies to identify ORIs in the genomic sequences, however, these methods have used either extensive computations for execution, or have limited optimization for the large datasets. Herein, a model called ORI-Deep is proposed to identify ORIs from the multiple cell type genomic sequence benchmark data. An efficient method is proposed using a deep neural network to identify ORIs for four different eukaryotic species. For better representation of data, a feature vector is constructed using statistical moments for the training and testing of data and is further fed to a long short-term memory (LSTM) network. To prove the effectiveness of the proposed model, we applied several validation techniques at different levels to obtain seven accuracy metrics, and the accuracy score for self-consistency, 10-fold cross-validation, jackknife and the independent set test is observed to be 0.977, 0.948, 0.976 and 0.977, respectively. Based on the results, it can be concluded that ORI-Deep can efficiently predict the sites of origin replication in DNA sequence with high accuracy. Webserver for ORI-Deep is available at (https://share.streamlit.io/waqarhusain/orideep/main/app.py), whereas source code is available at (https://github.com/WaqarHusain/OriDeep).

Threat, challenges, and preparedness for future pandemics: A descriptive review of phylogenetic analysis based predictions

For centuries the world has been confronted with many infectious diseases, with a potential to turn into a pandemic posing a constant threat to human lives. Some of these pandemics occurred due to the emergence of new disease or re-emergence of previously known diseases with a few mutations. In such scenarios their optimal prevention and control options were not adequately developed. Most of these diseases are highly contagious and for their timely control, knowledge about the pathogens and disease progression is the basic necessity. In this review, we have presented a documented chronology of the earlier pandemics, evolutionary analysis of the infectious disease with pandemic potential, the role of RNA, difficulties in controlling pandemics, and the likely pathogens that could trigger future pandemics. In this study, the evolutionary history of the pathogens was identified by carrying out phylogenetic analysis. The percentage similarity between different infectious diseases is critically analysed for the identification of their correlation using online sequence matcher tools. The Baltimore classification system was used for finding the genomic nature of the viruses. It was observed that most of the infectious pathogens rise from their animal hosts with some mutations in their genome composition. The phylogenetic tree shows that the single-stranded RNA diseases have a common origin and many of them are having high similarity percentage. The outcomes of this study will help in the identification of potential pathogens that can cause future pandemics. This information will be helpful in the development of early detection techniques, devising preventive mechanism to limit their spread, prophylactic measures, Infection control and therapeutic options, thereby, strengthening our approach towards global preparedness against future pandemics.

Incorporating Omics Data in Genomic Prediction

In this chapter, we discuss the motivation for integrating other types of omics data into genomic prediction methods. We give an overview of literature investigating the performance of omics-enhanced predictions, and highlight potential pitfalls when applying these methods in breeding. We emphasize that the statistical methods available for genomic data can be transferred to the general omics case. However, when using a framework of omic relationship matrices, the standardization of the variables may be more relevant than it is for a genomic relationship matrix based on single-nucleotide polymorphisms.

Synthesis and structure elucidation of the human tRNA nucleoside mannosyl-queuosine

Queuosine (Q) is a structurally complex, non-canonical RNA nucleoside. It is present in many eukaryotic and bacterial species, where it is part of the anticodon loop of certain tRNAs. In higher vertebrates, including humans, two further modified queuosine-derivatives exist - galactosyl- (galQ) and mannosyl-queuosine (manQ). The function of these low abundant hypermodified RNA nucleosides remains unknown. While the structure of galQ was elucidated and confirmed by total synthesis, the reported structure of manQ still awaits confirmation. By combining total synthesis and LC-MS-co-injection experiments, together with a metabolic feeding study of labelled hexoses, we show here that the natural compound manQ isolated from mouse liver deviates from the literature-reported structure. Our data show that manQ features an α-allyl connectivity of its sugar moiety. The yet unidentified glycosylases that attach galactose and mannose to the Q-base therefore have a maximally different constitutional connectivity preference. Knowing the correct structure of manQ will now pave the way towards further elucidation of its biological function.

Towards organism-level systems biology by next-generation genetics and whole-organ cell profiling

The system-level identification and analysis of molecular and cellular networks in mammals can be accelerated by "next-generation" genetics, which is defined as genetics that can achieve desired genetic makeup in a single generation without any animal crossing. We recently established a highly efficient procedure for producing knock-out (KO) mice using the "Triple-CRISPR" method, which targets a single gene by triple gRNAs in the CRISPR/Cas9 system. This procedure achieved an almost perfect KO efficiency (96-100%). We also established a highly efficient procedure, the "ES-mouse" method, for producing knock-in (KI) mice within a single generation. In this method, ES cells were treated with three inhibitors to keep their potency and then injected into 8-cell-stage embryos. These procedures dramatically shortened the time required to produce KO or KI mice from years down to about 3 months. The produced KO and KI mice can also be systematically profiled at a single-cell resolution by the "whole-organ cell profiling," which was realized by tissue-clearing methods, such as CUBIC, and an advanced light-sheet microscopy. The review describes the establishment and application of these technologies above in analyzing the three states (NREM sleep, REM sleep, and awake) of mammalian brains. It also discusses the role of calcium and muscarinic receptors in these states as well as the current challenges and future opportunities in the next-generation mammalian genetics and whole-organ cell profiling for organism-level systems biology.

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.