Neuronal subtype-specific transcriptomic changes in the cerebral neocortex associated with sleep pressure.

Sleep is homeostatically regulated by sleep pressure, which increases during wakefulness and dissipates during sleep. Recent studies have suggested that the cerebral neocortex, a six-layered structure composed of various layer- and projection-specific neuronal subtypes, is involved in the representation of sleep pressure governed by transcriptional regulation. Here, we examined the transcriptomic changes in neuronal subtypes in the neocortex upon increased sleep pressure using single-nucleus RNA sequencing datasets and predicted the putative intracellular and intercellular molecules involved in transcriptome alterations. We revealed that sleep deprivation (SD) had the greatest effect on the transcriptome of layer 2 and 3 intratelencephalic (L2/3 IT) neurons among the neocortical glutamatergic neuronal subtypes. The expression of mutant SIK3 (SLP), which is known to increase sleep pressure, also induced profound changes in the transcriptome of L2/3 IT neurons. We identified Junb as a candidate transcription factor involved in the alteration of the L2/3 IT neuronal transcriptome by SD and SIK3 (SLP) expression. Finally, we inferred putative intercellular ligands, including BDNF, LSAMP, and PRNP, which may be involved in SD-induced alteration of the transcriptome of L2/3 IT neurons. We suggest that the transcriptome of L2/3 IT neurons is most impacted by increased sleep pressure among neocortical glutamatergic neuronal subtypes and identify putative molecules involved in such transcriptional alterations.

DNA hypomethylation characterizes genes encoding tissue-dominant functional proteins in liver and skeletal muscle.

Each tissue has a dominant set of functional proteins required to mediate tissue-specific functions. Epigenetic modifications, transcription, and translational efficiency control tissue-dominant protein production. However, the coordination of these regulatory mechanisms to achieve such tissue-specific protein production remains unclear. Here, we analyzed the DNA methylome, transcriptome, and proteome in mouse liver and skeletal muscle. We found that DNA hypomethylation at promoter regions is globally associated with liver-dominant or skeletal muscle-dominant functional protein production within each tissue, as well as with genes encoding proteins involved in ubiquitous functions in both tissues. Thus, genes encoding liver-dominant proteins, such as those involved in glycolysis or gluconeogenesis, the urea cycle, complement and coagulation systems, enzymes of tryptophan metabolism, and cytochrome P450-related metabolism, were hypomethylated in the liver, whereas those encoding-skeletal muscle-dominant proteins, such as those involved in sarcomere organization, were hypomethylated in the skeletal muscle. Thus, DNA hypomethylation characterizes genes encoding tissue-dominant functional proteins.

Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.

BACKGROUND: Transcription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored. RESULTS: Here, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types. CONCLUSIONS: Our study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans.

Association of B7H3 and B7H4 gene polymorphisms and protein expression with the development and prognosis of autoimmune thyroid diseases.

BACKGROUND: The prognosis of autoimmune thyroid diseases (AITDs), including Graves' disease (GD) and Hashimoto's disease (HD), varies among patients. B7-H3 and B7-H4, members of the B7 family of proteins, regulate immune response. To clarify the association of B7-H3 and B7-H4 with the pathogenesis and prognosis of AITDs, we examined the expression of the soluble and membrane form of B7-H3 and B7-H4 and genotyped single nucleotide polymorphisms (SNPs) in the B7H3 and B7H4 genes. METHODS: We examined the expression of the membrane form of B7-H3 and B7-H4 by flow cytometry and their soluble forms by enzyme-linked immunosorbent assay. We genotyped SNPs in B7H3 and B7H4 in 187 GD patients, 217 HD patients, and 110 healthy volunteers using the PCR-RFLP method. RESULTS: The frequency of the B7H3 rs3816661 CC genotype was higher in patients with severe HD. G carriers of B7H4 rs10754339 A/G and B7H4 rs13505 T/G were more frequent in patients with AITD. A carrier of B7H4 rs10158166 A/G and C carriers of B7H4 rs3806373 C/T were more frequent in patients with intractable GD. The proportion of B7-H3+ monocytes was higher in the CC genotype of B7H3 rs3816661 C/T than in the other genotypes and was lower in patients with GD and HD than in healthy controls. The concentration of soluble B7-H4 was lower in the TG genotype of B7H4 rs13505 T/G than in the TT genotype and was higher in patients with AITD than in healthy controls. CONCLUSION: B7H3 and B7H4 are associated with AITD susceptibility and prognosis.

Granulocyte Colony-stimulating Factor-associated Aortitis on Gallium Scintigraphy.

Aortitis is a rare adverse event associated with granulocyte colony-stimulating factor (G-CSF). Contrast-enhanced computed tomography (CECT) is widely used to diagnose G-CSF-associated aortitis. However, the usefulness of gallium scintigraphy for the diagnosis of G-CSF-associated aortitis is unknown. We herein report a set of pre- and post-treatment gallium scintigrams of a patient with G-CSF-associated aortitis. During the diagnosis, gallium scintigraphy revealed hot spots on the arterial walls that appeared inflamed on CECT. Both the CECT and gallium scintigraphy findings disappeared. Gallium scintigraphy can be a supportive diagnostic tool for G-CSF-associated aortitis, especially in patients with an impaired renal function or allergy to iodine contrast.

SAGAS: Simulated annealing and greedy algorithm scheduler for laboratory automation.

During laboratory automation of life science experiments, coordinating specialized instruments and human experimenters for various experimental procedures is important to minimize the execution time. In particular, the scheduling of life science experiments requires the consideration of time constraints by mutual boundaries (TCMB) and can be formulated as the "scheduling for laboratory automation in biology" (S-LAB) problem. However, existing scheduling methods for the S-LAB problems have difficulties in obtaining a feasible solution for large-size scheduling problems at a time sufficient for real-time use. In this study, we proposed a fast schedule-finding method for S-LAB problems, SAGAS (Simulated annealing and greedy algorithm scheduler). SAGAS combines simulated annealing and the greedy algorithm to find a scheduling solution with the shortest possible execution time. We have performed scheduling on real experimental protocols and shown that SAGAS can search for feasible or optimal solutions in practicable computation time for various S-LAB problems. Furthermore, the reduced computation time by SAGAS enables us to systematically search for laboratory automation with minimum execution time by simulating scheduling for various laboratory configurations. This study provides a convenient scheduling method for life science automation laboratories and presents a new possibility for designing laboratory configurations.

Automation of yeast spot assays using an affordable liquid handling robot.

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experiments from preparing a dilution series to spotting to observing spots continuously has been implemented based on large laboratory automation devices and robots, especially for high-throughput functional screening assays. However, there has yet to be an affordable solution for the automated spot assays suited to researchers in average laboratories and with high customizability for end-users. To make reproducible spot assay experiments widely available, we have automated the plate-based yeast spot assay of budding yeast using Opentrons OT-2 (OT-2), an affordable liquid-handling robot, and a flatbed scanner. We prepared a 3D-printed mount for the Petri dish to allow for precise placement of the Petri dish inside the OT-2. To account for the uneven height of the agar plates, which were made by human hands, we devised a method to adjust the z-position of the pipette tips based on the weight of each agar plate. During the incubation of the agar plates, a flatbed scanner was used to automatically take images of the agar plates over time, allowing researchers to quantify and compare the cell density within the spots at optimal time points a posteriori. Furthermore, the accuracy of the newly developed automated spot assay was verified by performing spot assays with human experimenters and the OT-2 and quantifying the yeast-grown area of the spots. This study will contribute to the introduction of automated spot assays and the automated acquisition of growth processes in conventional laboratories that are not adapted for high-throughput laboratory automation.

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells.

MOTIVATION: Cell-cell communications regulate internal cellular states, e.g. gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation of cell-to-cell expression variability of HVGs via cell-cell communications is still largely unexplored. The recent advent of spatial transcriptome methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels influenced by neighboring cell types. However, limitations remain in the quantitativeness and interpretability: they neither focus on HVGs nor consider the effects of multiple neighboring cell types. RESULTS: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated the effects of multiple neighboring cell types on HVGs. Furthermore, applications to the two real datasets demonstrate that CCPLS can extract biologically interpretable insights from the inferred cell-cell communications. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/bioinfo-tsukuba/CCPLS. The data are available at https://github.com/bioinfo-tsukuba/CCPLS_paper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states.

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and leptin-deficient obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity.

Identifying potential regulators of JAGGED1 expression in portal mesenchymal cells.

OBJECTIVE: Portal mesenchymal cells induce the epithelial differentiation of the bile ducts in the developing liver via one of the Delta-Notch signaling components, JAGGED1. Although this differential induction is crucial for normal liver physiology as its genetic disorder (Alagille syndrome) causes jaundice, the molecular mechanism behind JAGGED1 expression remains unknown. Here, we searched for upstream regulatory transcription factors of JAGGED1 using an integrated bioinformatics method. RESULTS: According to the DoRothEA database, which integrates multiple lines of evidence on the relationship between transcription factors and their downstream target genes, three transcription factors were predicted to be upstream of JAGGED1: SLUG, SOX2, and EGR1. Among these, SLUG and EGR1 were enriched in ACTA2-expressing portal mesenchymal cells in two previously reported human fetal liver single-cell RNA-seq datasets. JAGGED1-expressing portal mesenchymal cells tended to express SLUG rather than EGR1, supporting that SLUG induced JAGGED1 expression. Together with the higher confidentiality of SLUG (DoRothEA level A) over EGR1 (DoRothEA level D), we concluded that SLUG was one of the most important candidate transcription factors upstream of JAGGED1. These results add mechanistic insights into the developmental biology of how portal mesenchymal cells support biliary development in the liver.

scGR-seq: Integrated analysis of glycan and RNA in single cells.

Glycans are structurally diverse molecules found on the surface of living cells. The protocol details a system developed for combined analysis of glycan and RNA in single cells (scGR-seq) using human induced pluripotent stem cells (hiPSCs) and hiPSC-derived neural progenitor cells (NPCs). scGR-seq consists of DNA-barcoded lectin-based glycan profiling by sequencing (scGlycan-seq) and single-cell transcriptome profiling (scRNA-seq). scGR-seq will be an essential technique to delineate the cellular heterogeneity of glycans across multicellular systems. For complete details on the use and execution of this profile, please refer to Minoshima et al. (2021).

Single-cell transcriptome analysis illuminating the characteristics of species-specific innate immune responses against viral infections.

BACKGROUND: Bats harbor various viruses without severe symptoms and act as their natural reservoirs. The tolerance of bats against viral infections is assumed to originate from the uniqueness of their immune system. However, how immune responses vary between primates and bats remains unclear. Here, we characterized differences in the immune responses by peripheral blood mononuclear cells to various pathogenic stimuli between primates (humans, chimpanzees, and macaques) and bats (Egyptian fruit bats) using single-cell RNA sequencing. RESULTS: We show that the induction patterns of key cytosolic DNA/RNA sensors and antiviral genes differed between primates and bats. A novel subset of monocytes induced by pathogenic stimuli specifically in bats was identified. Furthermore, bats robustly respond to DNA virus infection even though major DNA sensors are dampened in bats. CONCLUSIONS: Overall, our data suggest that immune responses are substantially different between primates and bats, presumably underlying the difference in viral pathogenicity among the mammalian species tested.

Cell-to-cell interaction analysis of prognostic ligand-receptor pairs in human pancreatic ductal adenocarcinoma.

Cell-to-cell interactions (CCIs) through ligand-receptor (LR) pairs in the tumor microenvironment underlie the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). However, there is scant knowledge of the association of CCIs with PDAC prognosis, which is critical to the identification of potential therapeutic candidates. Here, we sought to identify the LR pairs associated with PDAC patient prognosis by integrating survival analysis and single-cell CCI prediction. Via survival analysis using gene expression from cancer cohorts, we found 199 prognostic LR pairs. CCI prediction based on single-cell RNA-seq data revealed the enriched LR pairs associated with poor prognosis. Notably, the CCIs involved epithelial tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages through integrin-related and ANXA1-FPR pairs. Finally, we determined that CCIs involving 33 poor-prognostic LR pairs were associated with tumor grade. Although the clinical implication of the set of LR pairs must be determined, our results may provide potential therapeutic targets in PDAC.

Integrated analysis of glycan and RNA in single cells.

Single-cell sequencing has emerged as an indispensable technology to dissect cellular heterogeneity but never been applied to the simultaneous analysis of glycan and RNA. Using oligonucleotide-labeled lectins, we first established lectin-based glycan profiling of single cells by sequencing (scGlycan-seq). We then combined the scGlycan-seq with single-cell transcriptome profiling for joint analysis of glycan and RNA in single cells (scGR-seq). Using scGR-seq, we analyzed the two modalities in human induced pluripotent stem cells (hiPSCs) before and after differentiation into neural progenitor cells at the single-cell resolution. The combination of RNA and glycan separated the two cell types clearer than either one of them. Furthermore, integrative analysis of glycan and RNA modalities in single cells found known and unknown lectins that were specific to hiPSCs and coordinated with neural differentiation. Taken together, we demonstrate that scGR-seq can reveal the cellular heterogeneity and biological roles of glycans across multicellular systems.

Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints.

In automated laboratories consisting of multiple different types of instruments, scheduling algorithms are useful for determining the optimal allocations of instruments to minimize the time required to complete experimental procedures. However, previous studies on scheduling algorithms for laboratory automation have not emphasized the time constraints by mutual boundaries (TCMBs) among operations, which is important in procedures involving live cells or unstable biomolecules. Here, we define the "scheduling for laboratory automation in biology" (S-LAB) problem as a scheduling problem for automated laboratories in which operations with TCMBs are performed by multiple different instruments. We formulate an S-LAB problem as a mixed-integer programming (MIP) problem and propose a scheduling method using the branch-and-bound algorithm. Simulations show that our method can find the optimal schedules of S-LAB problems that minimize overall execution time while satisfying the TCMBs. Furthermore, we propose the use of our scheduling method for the simulation-based design of job definitions and laboratory configurations.

Correction: Possible cross-feeding pathway of facultative methylotroph Methyloceanibacter caenitepidi Gela4 on methanotroph Methylocaldum marinum S8.

[This corrects the article DOI: 10.1371/journal.pone.0213535.].

Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle.

Systemic metabolic homeostasis is regulated by inter-organ metabolic cycles involving multiple organs. Obesity impairs inter-organ metabolic cycles, resulting in metabolic diseases. The systemic landscape of dysregulated inter-organ metabolic cycles in obesity has yet to be explored. Here, we measured the transcriptome, proteome, and metabolome in the liver and skeletal muscle and the metabolome in blood of fasted wild-type and leptin-deficient obese (ob/ob) mice, identifying components with differential abundance and differential regulation in ob/ob mice. By constructing and evaluating the trans-omic network controlling the differences in metabolic reactions between fasted wild-type and ob/ob mice, we provided potential mechanisms of the obesity-associated dysfunctions of metabolic cycles between liver and skeletal muscle involving glucose-alanine, glucose-lactate, and ketone bodies. Our study revealed obesity-associated systemic pathological mechanisms of dysfunction of inter-organ metabolic cycles.

Oncolytic activity of naturally attenuated herpes-simplex virus HF10 against an immunocompetent model of oral carcinoma.

Prognosis for advanced oral carcinoma remains poor. Oncolytic virotherapy uses replication-competent viruses to infect and kill only the tumor cells. However, it has been difficult to investigate the oncolytic activity of viruses against oral carcinomas in mouse models. This study established a mouse model of oral cancer and investigated the in vitro and in vivo anti-tumor effects of HF10, a highly attenuated, replication-competent herpes simplex virus (HSV)-1. Mouse tongue cancer was induced by injecting 4-nitroquinoline 1-oxide into the mouse tongue. The murine oral cancer cell line isolated from this tumor, named NMOC1, formed invasive carcinoma within a week when injected into mouse tongue. HF10 successfully infected, replicated, and spread in the cancer cells in vitro. HF10 was able to kill cancer cells isolated from human or mouse tongue tumor. HF10 injection into tongue carcinomas prolonged mouse survival without any side effects or weight loss. Intertumoral injection of GFP-expressing HF10 confirmed that viral spread was confined within the tumors. Immunohistochemical staining showed that HF10 induced infiltration of CD8-positive T cells around HSV-infected cells in the tumor mass, implying increased anti-tumor immunity. We successfully established an oral cancer cell line and showed that HF10 is a promising therapeutic agent for oral cancer.

Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity.

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.

Stability of RNA sequences derived from the coronavirus genome in human cells.

Most viruses inhibit the innate immune system and/or the RNA degradation processes of host cells to construct an advantageous intracellular environment for their survival. Characteristic RNA sequences within RNA virus genomes or RNAs transcribed from DNA virus genomes contribute toward this inhibition. In this study, we developed a method called "Fate-seq" to comprehensively identify the RNA sequences derived from RNA and DNA viruses, contributing RNA stability in the cells. We examined the stabilization activity of 5,924 RNA fragments derived from 26 different viruses (16 RNA viruses and 10 DNA viruses) using next-generation sequencing of these RNAs fused 3' downstream of GFP reporter RNA. With the Fate-seq approach, we detected multiple virus-derived RNA sequences that stabilized GFP reporter RNA, including sequences derived from severe acute respiratory syndrome-related coronavirus (SARS-CoV). Comparative genomic analysis revealed that these RNA sequences and their predicted secondary structures are highly conserved between SARS-CoV and the novel coronavirus, SARS-CoV-2, which is responsible for the global outbreak of the coronavirus-associated disease that emerged in December 2019 (COVID-19). These sequences have the potential to enhance the stability of viral RNA genomes, thereby augmenting viral replication efficiency and virulence.