Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes

The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification

Understanding interaction mechanisms within cells, tissues, and organisms is crucial for driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating such biological systems. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks. The development of sequencing techniques and computational tools has recently enabled multiscale models. Combining such larger scale network modeling with mechanistic modeling provides us with an opportunity to reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from mechanistic models to multiscale models that integrate multiple layers of cellular networks and discuss how they can be used to shed light on disease states and even wellness-related states. Additionally, we introduce several methods that increase the certainty and accuracy of model predictions. Thus, combining mechanistic models with emerging mathematical and computational techniques can provide us with increasingly powerful tools to understand disease states and inspire drug discoveries.

SORFPP: Enhancing rich sequence-driven information to identify SEPs based on fused framework on validation datasets

BACKGROUND

Genome sequencing has enabled us to find functional peptides encoded by short open read frames (sORFs) in long non-coding RNAs (lncRNAs). sORFs-encoded peptides (SEPs) regulate gene expression, signaling, and so on and have significant roles, unlike common peptides. Various computational methods have been proposed. However, there is a lack of contributive features and effective models. Therefore, a high-throughput computational method to predict SEPs is needed.

RESULTS

We propose a computational method, SORFPP, to predict SEPs by mining feature information from multiple perspectives in an experimentally validated dataset from TranLnc. SORFPP fully extracts SEP sequence information using the protein language model ESM-2 and curated traditional encoding, including QSOrder, k-mer, etc. SORFPP uses CatBoost to solve the sparsity problem of traditional encoding. SORFPP also analyzes ESM-2 pre-training characterization information with the Self-attention model. Finally, an ensemble learning framework combines the two models and their results are fed into Logistic Regression model for accurate and robust predictions. For comparison, SORFPP outperforms other state-of-the-art models in Matthew correlation coefficient by 12.2%-24.2% on three benchmark datasets.

CONCLUSION

Integrating the ensemble learning strategy with contributive traditional features and the protein language encoding methods shows better performance. Datasets and codes are accessible at https://doi.org/10.6084/m9.figshare.28079897 and http://111.229.198.94:5000/.

Three- and four-stranded nucleic acid structures and their ligands

Nucleic acids have the potential to form not only duplexes, but also various non-canonical secondary structures in living cells. Non-canonical structures play regulatory functions mainly in the central dogma. Therefore, nucleic acid targeting molecules are potential novel therapeutic drugs that can target 'undruggable' proteins in various diseases. One of the concerns of small molecules targeting nucleic acids is selectivity, because nucleic acids have only four different building blocks. Three- and four-stranded non-canonical structures, triplexes and quadruplexes, respectively, are promising targets of small molecules because their three-dimensional structures are significantly different from the canonical duplexes, which are the most abundant in cells. Here, we describe some basic properties of the triplexes and quadruplexes and small molecules targeting the triplexes and tetraplexes.

Advancements in Single-Cell Proteomics and Mass Spectrometry-Based Techniques for Unmasking Cellular Diversity in Triple Negative Breast Cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms.

METHODS

The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations.

RESULTS

Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms.

CONCLUSION

Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC.

A Review of Protein Inference

Protein inference is an often neglected though crucial step in most proteomic experiments. In the bottom-up proteomic approach, the actual molecules of interest, the proteins, are digested into peptides before measurement on a mass spectrometer. This approach introduces a loss of information: The actual proteins must be inferred based on the identified peptides. While this might seem trivial, there are certain problems, one of the biggest being the presence of peptides that are shared among proteins. These amino acid sequences can, based on the database used for identification, belong to more than one protein. If such peptides are identified in a sample, it cannot be said which proteins actually were in the sample, but only an estimate on the most probable proteins or protein groups can be given based on a predefined inference strategy.Here we describe the effect of the chosen database for peptide identification on the number of shared peptides. Afterward, the mainly used protein inference methods will be sketched, and the necessity of stringent false discovery rate on peptide and protein level is discussed. Finally, we explain how the tool "PIA or protein inference algorithms" can be used together with the workflow environment KNIME and OpenMS to perform protein inference in a common proteomic experiment.

More than 2,500 coding genes in the human reference gene set still have unsettled status

In 2018 we analysed the three main repositories for the human proteome, Ensembl/GENCODE, RefSeq and UniProtKB. They disagreed on the coding status of one of every eight annotated coding genes. The analysis inspired bilateral collaborations between annotation groups. Here we have repeated our analysis with updated versions of the three reference coding gene sets. Superficially, little appears to have changed. Although there are slightly fewer genes predicted as coding overall, the three groups still disagree on the status of 2,606 annotated genes. However, a comparison without read-through genes and immunoglobulin fragments shows that the three reference sets have merged or reclassified more than 700 genes since the last analysis and that just 0.6% of Ensembl/GENCODE coding genes are not also annotated by the other two reference sets. We used eight features indicative of non-coding genes to examine the 21,873 coding genes annotated across the three reference sets. We found that more than 2,000 had one or more potential non-coding features. While some of these genes will be protein coding, we believe that most are likely to be non-coding genes or pseudogenes. Our results suggest that annotators still vastly overestimate the number of true coding genes.

The influence of lifestyle and environmental factors on host resilience through a homeostatic skin microbiota: An EAACI Task Force Report

Human skin is colonized with skin microbiota that includes commensal bacteria, fungi, arthropods, archaea and viruses. The composition of the microbiota varies at different anatomical locations according to changes in body temperature, pH, humidity/hydration or sebum content. A homeostatic skin microbiota is crucial to maintain epithelial barrier functions, to protect from invading pathogens and to interact with the immune system. Therefore, maintaining homeostasis holds promise to be an achievable goal for microbiome-directed treatment strategies as well as a prophylactic strategy to prevent the development of skin diseases, as dysbiosis or disruption of homeostatic skin microbiota is associated with skin inflammation. A healthy skin microbiome is likely modulated by genetic as well as environmental and lifestyle factors. In this review, we aim to provide a complete overview of the lifestyle and environmental factors that can contribute to maintaining the skin microbiome healthy. Awareness of these factors could be the basis for a prophylactic strategy to prevent the development of skin diseases or to be used as a therapeutic approach.

Harnessing Computational Approaches for RNA-Targeted Drug Discovery

RNA molecules have emerged as promising therapeutic targets due to their diverse functional and regulatory roles within cells. Computational modeling in RNA-targeted drug discovery presents a significant opportunity to expedite the discovery of novel small molecule compounds. However, this field encounters unique challenges compared to protein-targeted drug design, primarily due to limited experimental data availability and current models' inability to adequately address RNA's conformational flexibility during ligand recognition. Despite these challenges, several studies have successfully identified active RNA-targeting compounds using structure-based approaches or quantitative structure-activity relationship (QSAR) models. This review offers an overview of recent advancements in modeling RNA-small molecule interactions, emphasizing practical applications of computational methods in RNA-targeted drug discovery. Additionally, we survey existing databases that catalog nucleic acid-small molecule interactions. As interest in RNA-small molecule interactions grows and curated databases expand, the field anticipates rapid development. Novel computational models are poised to enhance the identification of potent and selective small-molecule modulators for therapeutic needs.

A deep audit of the PeptideAtlas database uncovers evidence for unannotated coding genes and aberrant translation

The human genome has been the subject of intense scrutiny by experimental and manual curation projects for more than two decades. Novel coding genes have been proposed from large-scale RNASeq, ribosome profiling and proteomics experiments. Here we carry out an in-depth analysis of an entire proteomics database. We analysed the proteins, peptides and spectra housed in the human build of the PeptideAtlas proteomics database to identify coding regions that are not yet annotated in the GENCODE reference gene set. We find support for hundreds of missing alternative protein isoforms and unannotated upstream translations, and evidence of cross-contamination from other species. There was reliable peptide evidence for 34 novel unannotated open reading frames (ORFs) in PeptideAtlas. We find that almost half belong to coding genes that are missing from GENCODE and other reference sets. Most of the remaining ORFs were not conserved beyond human, however, and their peptide confirmation was restricted to cancer cell lines. We show that this is strong evidence for aberrant translation, raising important questions about the extent of aberrant translation and how these ORFs should be annotated in reference genomes.

Copy number amplification-induced overexpression of lncRNA LOC101927668 facilitates colorectal cancer progression by recruiting hnRNPD to disrupt RBM47/p53/p21 signaling

BACKGROUND

Somatic copy number alterations (SCNAs) are pivotal in cancer progression and patient prognosis. Dysregulated long non-coding RNAs (lncRNAs), modulated by SCNAs, significantly impact tumorigenesis, including colorectal cancer (CRC). Nonetheless, the functional significance of lncRNAs induced by SCNAs in CRC remains largely unexplored.

METHODS

The dysregulated lncRNA LOC101927668, induced by copy number amplification, was identified through comprehensive bioinformatic analyses utilizing multidimensional data. Subsequent in situ hybridization was employed to ascertain the subcellular localization of LOC101927668, and gain- and loss-of-function experiments were conducted to elucidate its role in CRC progression. The downstream targets and signaling pathway influenced by LOC101927668 were identified and validated through a comprehensive approach, encompassing RNA sequencing, RT-qPCR, Western blot analysis, dual-luciferase reporter assay, evaluation of mRNA and protein degradation, and rescue experiments. Analysis of AU-rich elements (AREs) within the mRNA 3' untranslated region (UTR) of the downstream target, along with exploration of putative ARE-binding proteins, was conducted. RNA pull-down, mass spectrometry, RNA immunoprecipitation, and dual-luciferase reporter assays were employed to elucidate potential interacting proteins of LOC101927668 and further delineate the regulatory mechanism between LOC101927668 and its downstream target. Moreover, subcutaneous xenograft and orthotopic liver xenograft tumor models were utilized to evaluate the in vivo impact of LOC101927668 on CRC cells and investigate its correlation with downstream targets.

RESULTS

Significantly overexpressed LOC101927668, driven by chr7p22.3-p14.3 amplification, was markedly correlated with unfavorable clinical outcomes in our CRC patient cohort, as well as in TCGA and GEO datasets. Moreover, we demonstrated that enforced expression of LOC101927668 significantly enhanced cell proliferation, migration, and invasion, while its depletion impeded these processes in a p53-dependent manner. Mechanistically, nucleus-localized LOC101927668 recruited hnRNPD and translocated to the cytoplasm, accelerating the destabilization of RBM47 mRNA, a transcription factor of p53. As a nucleocytoplasmic shuttling protein, hnRNPD mediated RBM47 destabilization by binding to the ARE motif within RBM47 3'UTR, thereby suppressing the p53 signaling pathway and facilitating CRC progression.

CONCLUSIONS

The overexpression of LOC101927668, driven by SCNAs, facilitates CRC proliferation and metastasis by recruiting hnRNPD, thus perturbing the RBM47/p53/p21 signaling pathway. These findings underscore the pivotal roles of LOC101927668 and highlight its therapeutic potential in anti-CRC interventions.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Evidence for widespread translation of 5' untranslated regions

Ribosome profiling experiments support the translation of a range of novel human open reading frames. By contrast, most peptides from large-scale proteomics experiments derive from just one source, 5' untranslated regions. Across the human genome we find evidence for 192 translated upstream regions, most of which would produce protein isoforms with extended N-terminal ends. Almost all of these N-terminal extensions are from highly abundant genes, which suggests that the novel regions we detect are just the tip of the iceberg. These upstream regions have characteristics that are not typical of coding exons. Their GC-content is remarkably high, even higher than 5' regions in other genes, and a large majority have non-canonical start codons. Although some novel upstream regions have cross-species conservation - five have orthologues in invertebrates for example - the reading frames of two thirds are not conserved beyond simians. These non-conserved regions also have no evidence of purifying selection, which suggests that much of this translation is not functional. In addition, non-conserved upstream regions have significantly more peptides in cancer cell lines than would be expected, a strong indication that an aberrant or noisy translation initiation process may play an important role in translation from upstream regions.

Developmental dyslexia genes are selectively targeted by diverse environmental pollutants

BACKGROUND

Developmental dyslexia, a complex neurodevelopmental disorder, not only affects children's academic performance but is also associated with increased healthcare costs, lower employment rates, and reduced productivity. The pathogenesis of dyslexia remains unclear and it is generally considered to be caused by the overlap of genetic and environmental factors. Systematically exploring the close relationship between exposure to environmental compounds and susceptibility genes in the development of dyslexia is currently lacking but high necessary.

METHODS

In this study, we systematically compiled 131 publicly reported susceptibility genes for dyslexia sourced from DisGeNET, OMIM, and GeneCards databases. Comparative Toxicogenomics Database database was used to explore the overlap between susceptibility genes and 95 environmental compounds, including metals, persistent organic pollutants, polycyclic aromatic hydrocarbons, and pesticides. Chemical bias towards the dyslexia risk genes was taken into account in the observation/expectation ratios > 1 and the corresponding P value obtained by hypergeometric probability test.

RESULTS

Our study found that the number of dyslexia risk genes targeted by each chemical varied from 1 to 109. A total of 35 chemicals were involved in chemical reactions with dyslexia-associated genes, with significant enrichment values (observed/expected dyslexia risk genes) ranging from 1.147 (Atrazine) to 66.901 (Dibenzo(a, h)pyrene).

CONCLUSION

The results indicated that dyslexia-associated genes were implicated in certain chemical reactions. However, these findings are exploratory, and further research involving animal or cellular experiments is needed.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Regulation of Zbp1 by miR-99b-5p in microglia controls the development of schizophrenia-like symptoms in mice

Current approaches to the treatment of schizophrenia have mainly focused on the protein-coding part of the genome; in this context, the roles of microRNAs have received less attention. In the present study, we analyze the microRNAome in the blood and postmortem brains of schizophrenia patients, showing that the expression of miR-99b-5p is downregulated in both the prefrontal cortex and blood of patients. Lowering the amount of miR-99b-5p in mice leads to both schizophrenia-like phenotypes and inflammatory processes that are linked to synaptic pruning in microglia. The microglial miR-99b-5p-supressed inflammatory response requires Z-DNA binding protein 1 (Zbp1), which we identify as a novel miR-99b-5p target. Antisense oligonucleotides against Zbp1 ameliorate the pathological effects of miR-99b-5p inhibition. Our findings indicate that a novel miR-99b-5p-Zbp1 pathway in microglia might contribute to the pathogenesis of schizophrenia.

The potential of RNA therapeutics in dermatology

Ribonucleic acid (RNA) therapeutics hold great potential for the advancement of dermatological treatments due to, among other reasons, the possibility of treating previously undruggable targets, high specificity with minimal side effects, and ability to include multiple RNA targets in a single product. Although there have been research relating to RNA therapeutics for decades, there have not been many products translated for clinical use until recently. This may be because of challenges to the application of RNA therapeutics, including the dearth of effective modes of delivery to the target, and rapid degradation of RNA in the human body and environment. This article aims to provide insight on (1) the wide-ranging possibilities of RNA therapeutics in the field of dermatology as well as (2) how key challenges can be addressed, so as to encourage the development of novel dermatological treatments. We also share our experience on how RNA therapeutics have been applied in the management of hypertrophic and keloid scars.

An introduction to RNA therapeutics and their potentials

RNA therapeutics is a biological term regarding the usage of RNA-based molecules for medical purposes. Thanks to the success of mRNA-vaccine production against COVID-19, RNA therapeutics has gained more and more attention and investigation from worldwide scientists. It is considered as one of the promising alternatives for conventional drugs. In this first chapter, we presented an overview of the history and perspectives of RNA therapeutics' development. This chapter also explained the underlying mechanisms of different RNA-based molecules, including antisense oligonucleotide, interfering RNA (iRNA), aptamer, and mRNA, from degrading mRNA to inactivating targeted protein. Although there are many advantages of RNA therapeutics, its challenges in designing RNA chemical structure and the delivery vehicle need to be discussed. We described advanced technologies in the development of drug delivery systems that are positively correlated to the efficacy of the drug. Our aim is to provide a general background of RNA therapeutics to the audience before introducing plenty of more detailed parts, including clinical applications in certain diseases in the following chapters of the "RNA therapeutics" book.

Long noncoding RNA: An emerging diagnostic and therapeutic target in kidney diseases

Long noncoding RNAs (lncRNAs) have critical roles in the development of many diseases including kidney disease. An increasing number of studies have shown that lncRNAs are involved in kidney development and that their dysregulation can result in distinct disease processes, including acute kidney injury, chronic kidney disease, and renal cell carcinoma. Understanding the roles of lncRNAs in kidney disease may provide new diagnostic and therapeutic opportunities in the clinic. This review provides an overview of lncRNA characteristics, and biological function and discusses specific studies that provide insight into the function and potential application of lncRNAs in kidney disease treatment.

Predicting the Structural Impact of Human Alternative Splicing

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

Shedding light on the dark genome: Insights into the genetic, CRISPR-based, and pharmacological dependencies of human cancers and disease aggressiveness

Investigating the human genome is vital for identifying risk factors and devising effective therapies to combat genetic disorders and cancer. Despite the extensive knowledge of the "light genome", the poorly understood "dark genome" remains understudied. In this study, we integrated data from 20,412 protein-coding genes in Pharos and 8,395 patient-derived tumours from The Cancer Genome Atlas (TCGA) to examine the genetic and pharmacological dependencies in human cancers and their treatment implications. We discovered that dark genes exhibited high mutation rates in certain cancers, similar to light genes. By combining the drug response profiles of cancer cells with cell fitness post-CRISPR-mediated gene knockout, we identified the crucial vulnerabilities associated with both dark and light genes. Our analysis also revealed that tumours harbouring dark gene mutations displayed worse overall and disease-free survival rates than those without such mutations. Furthermore, dark gene expression levels significantly influenced patient survival outcomes. Our findings demonstrated a similar distribution of genetic and pharmacological dependencies across the light and dark genomes, suggesting that targeting the dark genome holds promise for cancer treatment. This study underscores the need for ongoing research on the dark genome to better comprehend the underlying mechanisms of cancer and develop more effective therapies.

Exploring Giant Unilamellar Vesicle Production for Artificial Cells - Current Challenges and Future Directions

Creating an artificial cell from the bottom up is a long-standing challenge and, while significant progress has been made, the full realization of this goal remains elusive. Arguably, one of the biggest hurdles that researchers are facing now is the assembly of different modules of cell function inside a single container. Giant unilamellar vesicles (GUVs) have emerged as a suitable container with many methods available for their production. Well-studied swelling-based methods offer a wide range of lipid compositions but at the expense of limited encapsulation efficiency. Emulsion-based methods, on the other hand, excel at encapsulation but are only effective with a limited set of membrane compositions and may entrap residual additives in the lipid bilayer. Since the ultimate artificial cell will need to comply with both specific membrane and encapsulation requirements, there is still no one-method-fits-all solution for GUV formation available today. This review discusses the state of the art in different GUV production methods and their compatibility with GUV requirements and operational requirements such as reproducibility and ease of use. It concludes by identifying the most pressing issues and proposes potential avenues for future research to bring us one step closer to turning artificial cells into a reality.

Reconstituted in vitro systems to reveal the roles and functions of septins

Septins are essential cytoskeletal proteins involved in key cellular processes and have also been implicated in diseases from cancers to neurodegenerative pathologies. However, they have not been as thoroughly studied as other cytoskeletal proteins. In vivo, septins interact with other cytoskeletal proteins and with the inner plasma membrane. Hence, bottom-up in vitro cell-free assays are well suited to dissect the roles and behavior of septins in a controlled environment. Specifically, in vitro studies have been invaluable in describing the self-assembly of septins into a large diversity of ultrastructures. Given that septins interact specifically with membrane, the details of these septin-membrane interactions have been analyzed using reconstituted lipid systems. In particular, at a membrane, septins are often localized at curvatures of micrometer scale. In that context, in vitro assays have been performed with substrates of varying curvatures (spheres, cylinders or undulated substrates) to probe the sensitivity of septins to membrane curvature. This Review will first present the structural properties of septins in solution and describe the interplay of septins with cytoskeletal partners. We will then discuss how septins interact with biomimetic membranes and induce their reshaping. Finally, we will highlight the curvature sensitivity of septins and how they alter the mechanical properties of membranes.

Updated Considerations for the Immunopharmacological Aspects of the "Talented mRNA Vaccines"

Messenger RNA (mRNA) vaccines belong to a new class of medications, RNA therapeutics, including both coding and non-coding RNAs. The use of mRNA as a therapy is based on the biological role of mRNA itself, namely its translation into a functional protein. The goal of mRNA vaccines is to produce a specific antigen in cells to elicit an immune response that might be prophylactic or therapeutic. The potential of mRNA as vaccine has been envisaged for years but its efficacy has been clearly demonstrated with the approval of COVID-19 vaccines in 2021. Since then, mRNA vaccines have been in the pipeline for diseases that are still untreatable. There are many advantages of mRNA vaccines over traditional vaccines, including easy and cost-effective production, high safety, and high-level antigen expression. However, the nature of mRNA itself and some technical issues pose challenges associated with the vaccines' development and use. Here we review the immunological and pharmacological features of mRNA vaccines by discussing their pharmacokinetics, mechanisms of action, and safety, with a particular attention on the advantages and challenges related to their administration. Furthermore, we present an overview of the areas of application and the clinical trials that utilize a mRNA vaccine as a treatment.

Tailored machine learning models for functional RNA detection in genome-wide screens

The in silico prediction of non-coding and protein-coding genetic loci has received considerable attention in comparative genomics aiming in particular at the identification of properties of nucleotide sequences that are informative of their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases

The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.

MicroRNA-based therapeutics for inflammatory disorders of the microbiota-gut-brain axis

An emerging but less explored shared pathophysiology across microbiota-gut-brain axis disorders is aberrant miRNA expression, which may represent novel therapeutic targets. miRNAs are small, endogenous non-coding RNAs that are important transcriptional repressors of gene expression. Most importantly, they regulate the integrity of the intestinal epithelial and blood-brain barriers and serve as an important communication channel between the gut microbiome and the host. A well-defined understanding of the mode of action, therapeutic strategies and delivery mechanisms of miRNAs is pivotal in translating the clinical applications of miRNA-based therapeutics. Accumulating evidence links disorders of the microbiota-gut-brain axis with a compromised gut-blood-brain-barrier, causing gut contents such as immune cells and microbiota to enter the bloodstream leading to low-grade systemic inflammation. This has the potential to affect all organs, including the brain, causing central inflammation and the development of neurodegenerative and neuropsychiatric diseases. In this review, we have examined in detail miRNA biogenesis, strategies for therapeutic application, delivery mechanisms, as well as their pathophysiology and clinical applications in inflammatory gut-brain disorders. The research data in this review was drawn from the following databases: PubMed, Google Scholar, and Clinicaltrials.gov. With increasing evidence of the pathophysiological importance for miRNAs in microbiota-gut-brain axis disorders, therapeutic targeting of cross-regulated miRNAs in these disorders displays potentially transformative and translational potential. Further preclinical research and human clinical trials are required to further advance this area of research.

Large-Scale Plasma Proteome Epitome Profiling is an Efficient Tool for the Discovery of Cancer Biomarkers

Current proteomic technologies focus on the quantification of protein levels, while little effort is dedicated to the development of system approaches to simultaneously monitor proteome variability and abundance. Protein variants may display different immunogenic epitopes detectable by monoclonal antibodies. Epitope variability results from alternative splicing, posttranslational modifications, processing, degradation, and complex formation and possesses dynamically changing availability of interacting surface structures that frequently serve as reachable epitopes and often carry different functions. Thus, it is highly likely that the presence of some of the accessible epitopes correlates with function under physiological and pathological conditions. To enable the exploration of the impact of protein variation on the immunogenic epitome first, here, we present a robust and analytically validated PEP technology for characterizing immunogenic epitopes of the plasma. To this end, we prepared mAb libraries directed against the normalized human plasma proteome as a complex natural immunogen. Antibody producing hybridomas were selected and cloned. Monoclonal antibodies react with single epitopes, thus profiling with the libraries is expected to profile many epitopes which we define by the mimotopes, as we present here. Screening blood plasma samples from control subjects (n = 558) and cancer patients (n = 598) for merely 69 native epitopes displayed by 20 abundant plasma proteins resulted in distinct cancer-specific epitope panels that showed high accuracy (AUC 0.826-0.966) and specificity for lung, breast, and colon cancer. Deeper profiling (≈290 epitopes of approximately 100 proteins) showed unexpected granularity of the epitope-level expression data and detected neutral and lung cancer-associated epitopes of individual proteins. Biomarker epitope panels selected from a pool of 21 epitopes of 12 proteins were validated in independent clinical cohorts. The results demonstrate the value of PEP as a rich and thus far unexplored source of protein biomarkers with diagnostic potential.

Long non-coding RNAs in non-small cell lung cancer: implications for EGFR-TKI resistance

Non-small cell lung cancer (NSCLC) is one of the most common types of malignant tumors as well as the leading cause of cancer-related deaths in the world. The application of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) has dramatically improved the prognosis of NSCLC patients who harbor EGFR mutations. However, despite an excellent initial response, NSCLC inevitably becomes resistant to EGFR-TKIs, leading to irreversible disease progression. Hence, it is of great significance to shed light on the molecular mechanisms underlying the EGFR-TKI resistance in NSCLC. Long non-coding RNAs (lncRNAs) are critical gene modulators that are able to act as oncogenes or tumor suppressors that modulate tumorigenesis, invasion, and metastasis. Recently, extensive evidence demonstrates that lncRNAs also have a significant function in modulating EGFR-TKI resistance in NSCLC. In this review, we present a comprehensive summary of the lncRNAs involved in EGFR-TKI resistance in NSCLC and focus on their detailed mechanisms of action, including activation of alternative bypass signaling pathways, phenotypic transformation, intercellular communication in the tumor microenvironment, competing endogenous RNAs (ceRNAs) networks, and epigenetic modifications. In addition, we briefly discuss the limitations and the clinical implications of current lncRNAs research in this field.

Proteomics-based trapping with single or multiple inactive mutants reproducibly profiles histone deacetylase 1 substrates

Histone deacetylase 1 (HDAC1) plays a key role in diverse cellular processes. With the aberrant expression of HDAC1 linked to many diseases, including cancers, HDAC inhibitors have been used successfully as therapeutics. HDAC1 has been predominantly associated with histone deacetylation and gene expression. Recently, non-histone substrates have revealed diverse roles of HDAC1 beyond epigenetics. To augment discovery of non-histone substrates, we introduced "substrate trapping" to enrich HDAC1 substrates using an inactive mutant. Herein, we performed a series of proteomics studies to test the robustness of HDAC1 substrate trapping. Based on our recent results documenting that different HDAC1 mutants preferentially bound different substrates, which suggested that multiple mutants could be used for efficient trapping, trapping with three single point mutants simultaneously identified several potential substrates uniquely compared to a single mutant alone. However, a greater number of biologically interesting hits were observed using only a single mutant, which suggests that the C151A HDAC1 mutant is the optimal trap. Importantly, comparing independent trials with a single mutant performed by different experimentalists and HEK293 cell populations, trapping was robust and reproducible. Based on the reproducible trapping data, carnosine N-methyltransferase 1 (CARNMT1) was validated as an HDAC1 substrate. The data document that mutant trapping is an effective method for discovery of unanticipated HDAC substrates. SIGNIFICANCE: Histone deacetylase (HDAC) proteins are well established epigenetic transcriptional regulators that deacetylate histone substrates to control gene expression. More recently, deacetylation of non-histone substrates has linked HDAC activity to functions outside of epigenetics. Given the use of HDAC inhibitor drugs as anti-cancer therapeutics, understanding the full functions of HDAC proteins in cell biology is essential to future drug design. To discover unanticipated non-histone substrates and further characterize HDAC functions, inactive mutants have been used to "trap" putative substrates, which were identified with mass spectrometry-based proteomics analysis. Here multiple trapping studies were performed to test the robustness of using inactive mutants and proteomics for HDAC substrate discovery. The data confirm the value of trapping mutants as effective tools to discover HDAC substrates and link HDAC activity to unexpected biological functions.

A degradome-based prognostic signature that correlates with immune infiltration and tumor mutation burden in breast cancer

Introduction

Female breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.

Methods

A total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.

Results

A 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.

Conclusion

Multidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

Novel maternal autoantibodies in autism spectrum disorder: Implications for screening and diagnosis

Introduction

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which early recognition is a major challenge. Autoantibodies against fetal brain antigens have been found in the blood of mothers of children with ASD (m-ASD) and can be transferred to the fetus where they can impact neurodevelopment by binding to fetal brain proteins. This study aims to identify novel maternal autoantibodies reactive against human fetal brain antigens, and explore their use as biomarkers for ASD screening and diagnosis.

Methods

A custom-made human fetal brain cDNA phage display library was constructed, and screened for antibody reactivity in m-ASD samples from the Simons Simplex Collection (SSC) of the Simons Foundation Autism Research Initiative (SFARI). Antibody reactivity against 6 identified antigens was determined in plasma samples of 238 m-ASD and 90 mothers with typically developing children (m-TD).

Results

We identified antibodies to 6 novel University Hasselt (UH)-ASD antigens, including three novel m-ASD autoantigens, i.e., ribosomal protein L23 (RPL23), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and calmodulin-regulated spectrin-associated protein 3 (CAMSAP3). Antibody reactivity against a panel of four of these targets was found in 16% of m-ASD samples, compared to 4% in m-TD samples (p = 0.0049).

Discussion

Maternal antibodies against 4 UH-ASD antigens could therefore provide a novel tool to support the diagnosis of ASD in a subset of individuals.

A Clinical Cognizance of Molecular and Pathological Diagnostic Approach of TNBC

Genetic, transcriptional, and clinical heterogeneity of disease has remained to be a prominent obstacle to the development of a targeted therapeutic approach against TNBC. So far, based on tumor size, lymph node status, and histologic features TNBC subtypes were stratified. Insights into inter and intratumoral heterogeneity of TNBC were gained by next-generation sequencing, genomic, transcriptomic, proteomic, and clinicopathological characterization. To depict tumor response to neoadjuvant chemotherapy, radiological characterization may also a play significant role. Biomarkers for subtyping TNBC were highly needed to depict the survival outcome. This chapter discussed the available and possible molecular and pathological diagnostic approaches to TNBC. Furthermore, the integration of morphological and genomic data may emerge as a promising approach for the identification of new therapeutic and prognostic markers to predict the likely outcome of the disease. This chapter aims to highlight the molecular and pathological diagnostic approaches to depict both metastatic and non-metastatic TNBC.&nbsp;<br>

Noncoding RNA therapeutics for substance use disorder

Although noncoding RNAs (ncRNAs) have been shown to regulate maladaptive neuroadaptations that drive compulsive drug use, ncRNA-targeting therapeutics for substance use disorder (SUD) have yet to be clinically tested. Recent advances in RNA-based drugs have improved many therapeutic issues related to immune response, specificity, and delivery, leading to multiple successful clinical trials for other diseases. As the need for safe and effective treatments for SUD continues to grow, novel nucleic acid-based therapeutics represent an appealing approach to target ncRNA mechanisms in SUD. Here, we review ncRNA processes implicated in SUD, discuss recent therapeutic approaches for targeting ncRNAs, and highlight potential opportunities and challenges of ncRNA-targeting therapeutics for SUD.

Useful genes for predicting the efficacy of transarterial chemoembolization in hepatocellular carcinoma

Transarterial chemoembolization (TACE) is generally used to treat patients with hepatocellular carcinoma (HCC), a common and deadly cancer; however, its efficacy varies according to factors such as tumor volume, stage, serum alpha-fetoprotein level, and chosen feeding artery. In addition, gene-related factors have been recently suggested to be involved in the regulation and prediction of TACE outcomes. Accordingly, genes could serve as effective biomarkers to select patients who can benefit from TACE. These gene-related factors can activate signaling pathways affecting cancer cell survival while regulating the epithelial-mesenchymal transition, angiogenesis, and the tumor microenvironment, all directly associated with tumor progression, thereby affecting TACE efficacy. Moreover, this disordered gene expression is associated with poor prognosis in patients with HCC, including TACE resistance, postoperative recurrence, and metastasis. To identify the exact relationship between various genes and TACE efficacy, this review summarizes the involvement of protein-coding and non-coding genes and single nucleotide polymorphisms in TACE efficacy for predicting the efficacy of TACE; the present findings may help improve the efficacy of TACE in clinical settings.

Cathepsin K in Pathological Conditions and New Therapeutic and Diagnostic Perspectives

Cathepsin K (CatK) is a part of the family of cysteine proteases involved in many important processes, including the degradation activity of collagen 1 and elastin in bone resorption. Changes in levels of CatK are associated with various pathological conditions, primarily related to bone and cartilage degradation, such as pycnodysostosis (associated with CatK deficiency), osteoporosis, and osteoarthritis (associated with CatK overexpression). Recently, the increased secretion of CatK is being highly correlated to vascular inflammation, hypersensitivity pneumonitis, Wegener granulomatosis, berylliosis, tuberculosis, as well as with tumor progression. Due to the wide spectrum of diseases in which CatK is involved, the design and validation of active site-specific inhibitors has been a subject of keen interest in pharmaceutical companies in recent decades. In this review, we summarized the molecular background of CatK and its involvement in various diseases, as well as its clinical significance for diagnosis and therapy.

An overview of structural approaches to study therapeutic RNAs

RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.

Constructed the ceRNA network and predicted a FEZF1-AS1/miR-92b-3p/ZIC5 axis in colon cancer

The purpose of this study was to identify the role of FEZF1-AS1 in colon cancer and predicted the underlying mechanism. We extracted sequencing data of colon cancer patients from The Cancer Genome Atlas database, identified the differential expression of long noncoding RNA, microRNA, and messenger RNA, constructed a competitive endogenous RNA network, and then analyzed prognosis. Then, we used the enrichment analysis databases for functional analysis. Finally, we studied the FEZF1-AS1/miR-92b-3p/ZIC5 axis. We detected the expression of FEZF1-AS1, miR-92b-3p, and ZIC5 via quantitative reverse transcription-PCR, transfected colon cancer cell RKO with lentivirus and conducted FEZF1-AS1 knockdown, and performed cancer-related functional assays. It indicated that many RNA in the competitive endogenous RNA network, such as ZIC5, were predicted to be related to overall survival of colon cancer patients, and enrichment analysis showed cancer-related signaling pathways, such as PI3K/AKT signaling pathway. The expression of FEZF1-AS1 and ZIC5 was significantly higher and that of miR-92b-3p was lower in the colon cancer than in the normal colon tissues. FEZF1-AS1 promoted the migration, proliferation, as well as invasion of RKO. According to the prediction, FEZF1-AS1 and ZIC5 might competitively bind to miR-92b-3p, leading to the weakening of the inhibitory impact of miR-92b-3p on ZIC5 and increasing expression of ZIC5, thus further activating the PI3K/AKT signaling pathway, which led to the occurrence and development of colon cancer. The study suggested that FEZF1-AS1 might be an effective diagnosis biomarker for colon cancer.

NOX4 has the potential to be a biomarker associated with colon cancer ferroptosis and immune infiltration based on bioinformatics analysis

Background

Colon cancer (CC) is a common tumor, but its pathogenesis is still not well understood. Competitive endogenous RNA (ceRNA) theory, ferroptosis and tumor immune infiltration may be the mechanisms of the development of cancer. The purpose of the study is to seek genes connected with both immunity and ferroptosis, and provide important molecular basis for early noninvasive diagnosis and immunotherapy of CC.

Methods

We extracted messenger RNA (mRNA), microRNA (miRNA), and long noncoding RNA (lncRNA) data of CC from The Cancer Genome Atlas database (TCGA), identified the differentially expressed mRNA (DEmRNA), miRNA (DEmiRNA) and lncRNA (DElncRNA), then constructed a ceRNA network. Venn overlap analysis was used to identify genes associated with immunity and ferroptosis in ceRNA network. The expression and prognosis of target genes were analyzed via Gene Expression Profiling Interactive Analysis (GEPIA) and PrognoScan database, and we analysed the related functions and signaling pathways of target genes by enrichment analysis. The correlation between target genes and tumor immune infiltrating was explored by CIBERSORT and spearman correlation analysis. Finally, the expression of target genes was detected via quantitative reverse transcription-PCR (qRT-PCR) in CC and normal colon tissues.

Results

Results showed that there were 4 DElncRNA, 4 DEmiRNA and 126 DEmRNA in ceRNA network. NADPH oxidase 4 protein (NOX4) was a DEmRNA associated with immunity and ferroptosis in ceRNA network. NOX4 was highly expressed in CC and connected with unfavourable prognosis. NOX4 was obviously enriched in pathways connected with carcinogenesis and significantly correlated with six kinds of immune cells. Immune checkpoints and NOX4 spearman correlation analysis showed that the expression of NOX4 was positively related to programmed cell death protein 1 (PD-1)-PDCD1, programmed cell death-Ligand 1 (PD-L1)-CD274 and cytotoxic T-lymphocyte-associated protein 4 (CTLA4).

Conclusions

To conclude, our study suggests that NOX4 is associated with both ferroptosis and tumor immunity, and might be a biomarker associated with the carcinogenesis, prognosis of CC and a potential target of CC immunotherapy.

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in e.g. instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards, since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements. This article is protected by copyright. All rights reserved.

Browning of the white adipose tissue regulation: new insights into nutritional and metabolic relevance in health and diseases

Adipose tissues are dynamic tissues that play crucial physiological roles in maintaining health and homeostasis. Although white adipose tissue and brown adipose tissue are currently considered key endocrine organs, they differ functionally and morphologically. The existence of the beige or brite adipocytes, cells displaying intermediary characteristics between white and brown adipocytes, illustrates the plastic nature of the adipose tissue. These cells are generated through white adipose tissue browning, a process associated with augmented non-shivering thermogenesis and metabolic capacity. This process involves the upregulation of the uncoupling protein 1, a molecule that uncouples the respiratory chain from Adenosine triphosphate synthesis, producing heat. β-3 adrenergic receptor system is one important mediator of white adipose tissue browning, during cold exposure. Surprisingly, hyperthermia may also induce beige activation and white adipose tissue beiging. Physical exercising copes with increased levels of specific molecules, including Beta-Aminoisobutyric acid, irisin, and Fibroblast growth factor 21 (FGF21), which induce adipose tissue browning. FGF21 is a stress-responsive hormone that interacts with beta-klotho. The central roles played by hormones in the browning process highlight the relevance of the individual lifestyle, including circadian rhythm and diet. Circadian rhythm involves the sleep-wake cycle and is regulated by melatonin, a hormone associated with UCP1 level upregulation. In contrast to the pro-inflammatory and adipose tissue disrupting effects of the western diet, specific food items, including capsaicin and n-3 polyunsaturated fatty acids, and dietary interventions such as calorie restriction and intermittent fasting, favor white adipose tissue browning and metabolic efficiency. The intestinal microbiome has also been pictured as a key factor in regulating white tissue browning, as it modulates bile acid levels, important molecules for the thermogenic program activation. During embryogenesis, in which adipose tissue formation is affected by Bone morphogenetic proteins that regulate gene expression, the stimuli herein discussed influence an orchestra of gene expression regulators, including a plethora of transcription factors, and chromatin remodeling enzymes, and non-coding RNAs. Considering the detrimental effects of adipose tissue browning and the disparities between adipose tissue characteristics in mice and humans, further efforts will benefit a better understanding of adipose tissue plasticity biology and its applicability to managing the overwhelming burden of several chronic diseases.

Long Noncoding RNAs That Function in Nutrition: Lnc-ing Nutritional Cues to Metabolic Pathways

Long noncoding RNAs (lncRNAs) are sensitive to changing environments and play key roles in health and disease. Emerging evidence indicates that lncRNAs regulate gene expression to shape metabolic processes in response to changing nutritional cues. Here we review various lncRNAs sensitive to fasting, feeding, and high-fat diet in key metabolic tissues (liver, adipose, and muscle), highlighting regulatory mechanisms that trigger expression changes of lncRNAs themselves, and how these lncRNAs regulate gene expression of key metabolic genes in specific cell types or across tissues. Determining how lncRNAs respond to changes in nutrition is critical for our understanding of the complex downstream cascades following dietary changes and can shape how we treat metabolic disease. Furthermore, investigating sex biases that might influence lncRNA-regulated responses will likely reveal contributions toward the observed disparities between the sexes in metabolic diseases.

Underexpression of LINC00173 in TCF3/PBX1-Positive Cases Is Associated With Poor Prognosis in Children With B-Cell Precursor Acute Lymphoblastic Leukemia

Background

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most frequent pediatric cancer worldwide. Despite improvements in treatment regimens, approximately 20% of the cases cannot be cured, highlighting the necessity for identifying new biomarkers to improve the current clinical and molecular risk stratification schemes. We aimed to investigate whether LINC00173 is a biomarker in ALL and to explore its expression level in other human cancer types.

Methods

A nested case-control study including Mexican children with BCP-ALL was conducted. LINC00173 expression was evaluated by qRT-PCR using hydrolysis probes. To validate our findings, RNA-seq expression data from BCP-ALL and normal tissues were retrieved from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) repositories, respectively. LINC00173 expression was also evaluated in solid tumors by downloading available data from The Cancer Genome Atlas (TCGA).

Results

A lower expression of LINC00173 in BCP-ALL cases compared to normal subjects was observed (p < 0.05). ALL patients who carry the TCF3/PBX1 fusion gene displayed lower expression of LINC00173 in contrast to other BCP-ALL molecular subtypes (p < 0.04). LINC00173 underexpression was associated with a high risk to relapse (HR = 1.946, 95% CI = 1.213-3.120) and die (HR = 2.073, 95% CI = 1.211-3.547). Patients with TCF3/PBX1 and underexpression of LINC00173 had the worst prognosis (DFS: HR = 12.24, 95% CI = 5.04-29.71; OS: HR = 11.19, 95% CI = 26-32). TCGA data analysis revealed that underexpression of LINC00173 is also associated with poor clinical outcomes in six new reported tumor types.

Conclusion

Our findings suggest that LINC00173 is a biomarker of poor prognosis in BCP-ALL and other types of cancer. We observed an association between the expression of LINC00173 and TCF3/PBX1 and the risk to relapse and die in BCP-ALL, which is worse in TCF3/PBX1-positive cases displaying underexpression of LINC00173. Experimental studies are needed to provide insight into the LINC00173 and TCF3/PBX relationship.

Dysfunctional Heteroreceptor Complexes as Novel Targets for the Treatment of Major Depressive and Anxiety Disorders

Among mental diseases, major depressive disorder (MDD) and anxiety deserve a special place due to their high prevalence and their negative impact both on society and patients suffering from these disorders. Consequently, the development of novel strategies designed to treat them quickly and efficiently, without or at least having limited side effects, is considered a highly important goal. Growing evidence indicates that emerging properties are developed on recognition, trafficking, and signaling of G-protein coupled receptors (GPCRs) upon their heteromerization with other types of GPCRs, receptor tyrosine kinases, and ionotropic receptors such as N-methyl-D-aspartate (NMDA) receptors. Therefore, to develop new treatments for MDD and anxiety, it will be important to identify the most vulnerable heteroreceptor complexes involved in MDD and anxiety. This review focuses on how GPCRs, especially serotonin, dopamine, galanin, and opioid heteroreceptor complexes, modulate synaptic and volume transmission in the limbic networks of the brain. We attempt to provide information showing how these emerging concepts can contribute to finding new ways to treat both MDD and anxiety disorders.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

Compartment-Specific Proximity Ligation Expands the Toolbox to Assess the Interactome of the Long Non-Coding RNA NEAT1

The nuclear paraspeckle assembly transcript 1 (NEAT1) locus encodes two long non-coding (lnc)RNA isoforms that are upregulated in many tumours and dynamically expressed in response to stress. NEAT1 transcripts form ribonucleoprotein complexes with numerous RNA-binding proteins (RBPs) to assemble paraspeckles and modulate the localisation and activity of gene regulatory enzymes as well as a subset of messenger (m)RNA transcripts. The investigation of the dynamic composition of NEAT1-associated proteins and mRNAs is critical to understand the function of NEAT1. Interestingly, a growing number of biochemical and genetic tools to assess NEAT1 interactomes has been reported. Here, we discuss the Hybridisation Proximity (HyPro) labeling technique in the context of NEAT1. HyPro labeling is a recently developed method to detect spatially ordered interactions of RNA-containing nuclear compartments in cultured human cells. After introducing NEAT1 and paraspeckles, we describe the advantages of the HyPro technology in the context of other methods to study RNA interactomes, and review the key findings in mapping NEAT1-associated RNA transcripts and protein binding partners. We further discuss the limitations and potential improvements of HyPro labeling, and conclude by delineating its applicability in paraspeckles-related cancer research.