Long RNA-Mediated Chromatin Regulation in Fission Yeast and Mammals

As part of a complex network of genome control, long regulatory RNAs exert significant influences on chromatin dynamics. Understanding how this occurs could illuminate new avenues for disease treatment and lead to new hypotheses that would advance gene regulatory research. Recent studies using the model fission yeast Schizosaccharomyces pombe (S. pombe) and powerful parallel sequencing technologies have provided many insights in this area. This review will give an overview of key findings in S. pombe that relate long RNAs to multiple levels of chromatin regulation: histone modifications, gene neighborhood regulation in cis and higher-order chromosomal ordering. Moreover, we discuss parallels recently found in mammals to help bridge the knowledge gap between the study systems.

Recent Advances in RNA Therapy and Its Carriers to Treat the Single-Gene Neurological Disorders

The development of new sequencing technologies in the post-genomic era has accelerated the identification of causative mutations of several single gene disorders. Advances in cell and animal models provide insights into the underlining pathogenesis, which facilitates the development and maturation of new treatment strategies. The progress in biochemistry and molecular biology has established a new class of therapeutics—the short RNAs and expressible long RNAs. The sequences of therapeutic RNAs can be optimized to enhance their stability and translatability with reduced immunogenicity. The chemically-modified RNAs can also increase their stability during intracellular trafficking. In addition, the development of safe and high efficiency carriers that preserves the integrity of therapeutic RNA molecules also accelerates the transition of RNA therapeutics into the clinic. For example, for diseases that are caused by genetic defects in a specific protein, an effective approach termed “protein replacement therapy” can provide treatment through the delivery of modified translatable mRNAs. Short interference RNAs can also be used to treat diseases caused by gain of function mutations or restore the splicing aberration defects. Here we review the applications of newly developed RNA-based therapeutics and its delivery and discuss the clinical evidence supporting the potential of RNA-based therapy in single-gene neurological disorders.

The Application of Whole−Exome Sequencing in Patients With FUO

BackgroundFever of unknown origin (FUO) is still a challenge for clinicians. Next-generation sequencing technologies, such as whole exome sequencing (WES), can be used to identify genetic defects in patients and assist in diagnosis. In this study, we investigated the application of WES in individuals with FUO.MethodsWe performed whole-exome sequencing on 15 FUO patients. Clinical information was extracted from the hospital information system.ResultsIn 7/15 samples, we found positive results, including potentially causative mutations across eight different genes: CFTR, CD209, IRF2BP2, ADGRV 1, TYK2, MEFV, THBD and GATA2.ConclusionsOur results show that whole-exome sequencing can promote the genetic diagnosis and treatment of patients with FUO.

Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome

Background Next-generation sequencing (NGS) can identify mutations in the human genome that cause disease and has been widely adopted in clinical diagnosis. However, the human genome contains many polymorphic, low-complexity, and repetitive regions that are difficult to sequence and analyze. Despite their difficulty, these regions include many clinically important sequences that can inform the treatment of human diseases and improve the diagnostic yield of NGS. Results To evaluate the accuracy by which these difficult regions are analyzed with NGS, we built an in silico decoy chromosome, along with corresponding synthetic DNA reference controls, that encode difficult and clinically important human genome regions, including repeats, microsatellites, HLA genes, and immune receptors. These controls provide a known ground-truth reference against which to measure the performance of diverse sequencing technologies, reagents, and bioinformatic tools. Using this approach, we provide a comprehensive evaluation of short- and long-read sequencing instruments, library preparation methods, and software tools and identify the errors and systematic bias that confound our resolution of these remaining difficult regions. Conclusions This study provides an analytical validation of diagnosis using NGS in difficult regions of the human genome and highlights the challenges that remain to resolve these difficult regions.

The Interaction Between Long Non-Coding RNAs and Cancer-Associated Fibroblasts in Lung Cancer

Despite great advances in research and treatment, lung cancer is still one of the most leading causes of cancer-related deaths worldwide. Evidence is mounting that dynamic communication network in the tumor microenvironment (TME) play an integral role in tumor initiation and development. Cancer-associated fibroblasts (CAFs), which promote tumor growth and metastasis, are the most important stroma component in the tumor microenvironment. Consequently, in-depth identification of relevant molecular mechanisms and biomarkers related to CAFs will increase understanding of tumor development process, which is of great significance for precise treatment of lung cancer. With the development of sequencing technologies such as microarray and next-generation sequencing, lncRNAs without protein-coding ability have been found to act as communicators between tumor cells and CAFs. LncRNAs participate in the activation of normal fibroblasts (NFs) to CAFs. Moreover, activated CAFs can influence the gene expression and secretion characteristics of cells through lncRNAs, enhancing the malignant biological process in tumor cells. In addition, lncRNA-loaded exosomes are considered to be another important form of crosstalk between tumor cells and CAFs. In this review, we focus on the interaction between tumor cells and CAFs mediated by lncRNAs in the lung cancer microenvironment, and discuss the analysis of biological function and molecular mechanism. Furthermore, it contributes to paving a novel direction for the clinical treatment of lung cancer.

Complete Genomic Sequencing of Canine Distemper Virus With Nanopore Technology During an Epizootic Event

Canine distemper virus (CDV) endangers a wide range of wild animal populations and can cross species barriers, representing a significant conservational and animal health risk around the globe. During spring to autumn 2021, according to our current estimates a minimum of 50 wild live red foxes (Vulpes vulpes) died of CDV in Hungary, with CDV lesions. Oral, nasal and rectal swab samples were RT-PCR screened for Canine Distemper Virus from red fox carcasses. To investigate in more detail the origins of these CDV strains, 19 complete genomes were sequenced with a pan-genotype CDV-specific amplicon-based sequencing method developed by our laboratory and optimized for Oxford Nanopore Technologies platform. Phylogenetic analysis of the complete genomic sequences and separately the hemagglutinin gene sequences revealed the role of the Europe lineage of CDV as a causative agent for the current epizootic. Here we highlight the growing importance of fast developing rapid sequencing technologies to aid rapid response activities during epidemics or epizootic events. We also emphasize the urgent need for improved surveillance of CDV, considering the epizootic capability of enzootic strains as reported in the current study. For such future efforts, we provide a novel NGS protocol, which facilitates future genomic surveillance studies.

Omics Approaches to Assess Flavor Development in Cheese

Cheese is characterized by a rich and complex microbiota that plays a vital role during both production and ripening, contributing significantly to the safety, quality, and sensory characteristics of the final product. In this context, it is vital to explore the microbiota composition and understand its dynamics and evolution during cheese manufacturing and ripening. Application of high-throughput DNA sequencing technologies have facilitated the more accurate identification of the cheese microbiome, detailed study of its potential functionality, and its contribution to the development of specific organoleptic properties. These technologies include amplicon sequencing, whole-metagenome shotgun sequencing, metatranscriptomics, and, most recently, metabolomics. In recent years, however, the application of multiple meta-omics approaches along with data integration analysis, which was enabled by advanced computational and bioinformatics tools, paved the way to better comprehension of the cheese ripening process, revealing significant associations between the cheese microbiota and metabolites, as well as their impact on cheese flavor and quality.

MicrogliaST: a web server for microglia spatiotemportal pattern analysis in normal and disordered brains

Brain is the most complex organ of living organisms, as the celebrated cells in the brain, microglia play an indispensable role in the brain's immune microenvironment. Microglia have critical roles not only in neural development and homeostasis, but also in neurodegenerative diseases and malignant of the central nervous system. However, little is known about the dynamic characteristics of microglia during development or disease conditions. Recently, the single-cell RNA sequencing technologies have become possible to characterize the heterogeneity of immune system in brain. But it posed computational challenges on integrating and utilizing the massive published datasets to dissect the spatiotemporal characterization of microglia. Here, we present microgliaST (bio-bigdata.hrbmu.edu.cn/MST), a database consisting of single-cell microglia transcriptomes across multiple brain regions and developmental periods. Based on high-quality microglia markers collected from published papers, we annotated and constructed human and mouse transcriptomic profiles of 273,374 microglias, comprising 12 regions, 12 periods and 3 conditions (normal, disease, treatment). In addition, MicrogliaST provides multiple analytical tools to elucidate the landscape of microglia under disorder conditions, conduct personalized difference analysis and spatiotemporal dynamic analysis. More importantly, microgliaST paves an ingenious way to the study of brain environment, and also provides insights into clinical therapy assessments.

Predicting Physical Appearance from DNA Data—Towards Genomic Solutions

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics.

Single-Cell Proteomics Study of Immune Cell Diversity by Quantitating 465 Proteins

The identification and characterization of T cell subpopulations is critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced the single-cell genomics and transcriptomics for T cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in the protein format. Current single-cell proteomics technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein we present a cyclic multiplex in situ tagging (Cyclic MIST) technology to simultaneously measure 465 proteins, a scale of >10 times than similar technologies, in single cells. Such a high multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both THP1 and CD4+ T cells are analyzed by the Cyclic MIST technology, and over 300 surface markers have been profiled to classify the subpopulations. This represents the most comprehensive mapping of the diversity of immune cells at the protein level. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses, particularly cytokine storm that results in sepsis.