Identification of RNF213 as a Potential Suppressor of Local Invasion in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a lethal cancer with poor survival especially when it spreads. The histopathology of its rare intraductal papillary neoplasm of the bile duct type (IPNB) characteristically shows cancer cells originating within the confined bile duct space. These cells eventually invade and infiltrate the nearby liver tissues, making it a good model to study the mechanism of local invasion, which is the earliest step of metastasis. To discover potential suppressor genes of local invasion in ICC, we analyzed the somatic mutation profiles and performed clonal evolution analyses of the 11 pairs of macrodissected locally invasive IPNB tissues (LI-IPNB) and IPNB tissues without local invasion from the same patients. We identified a protein-truncating variant in an E3 ubiquitin ligase, RNF213 (c.6967C>T; p.Gln2323X; chr17: 78,319,102 [hg19], exon 29), as the most common protein-truncating variant event in LI-IPNB samples (4/11 patients). Knockdown of RNF213 in HuCCT1 and YSCCC cells showed increased migration and invasion, and reduced vasculogenic mimicry but maintained normal proliferation. Transcriptomic analysis of the RNF213-knockdown vs control cells was then performed in the HuCCT1, YSCCC, and KKU-100 cells. Gene ontology enrichment analysis of the common differentially expressed genes revealed significantly altered cytokine and oxidoreductase-oxidizing metal ion activities, as confirmed by Western blotting. Gene Set Enrichment Analysis identified the most enriched pathways being oxidative phosphorylation, fatty acid metabolism, reactive oxygen species, adipogenesis, and angiogenesis. In sum, loss-of-function mutation of RNF213 is a common genetic alteration in LI-IPNB tissues. RNF213 knockdown leads to increased migration and invasion of ICC cells, potentially through malfunctions of the pathways related to inflammation and energy metabolisms.

Large-scale comparative transcriptomic analysis of temperature-responsive genes in Arabidopsis thaliana.

KEY MESSAGE: Comparative transcriptomic analysis provides broad and detailed understandings of transcriptional responses to a wide range of temperatures in different plant tissues, and unique regulatory functions of temperature-mediating transcription factors. Climate change poses a great threat to plant diversity and food security. It is thus of necessity to understand the molecular mechanisms for perceiving and responding to adverse temperature changes, to develop the cultivars that are resilient to these environmental stresses. Making use of publicly available datasets, we gathered and re-analyzed 259 individual transcriptomic profiles from 139 unique experiments of Arabidopsis thaliana's shoot, root, and seedling tissues, subjected to a wide variety of temperature conditions, ranging from freezing, cold, low and high ambient temperatures, to heat shock. Despite the underlying differences in the overall transcriptomic profiles between the plant tissues, we were able to identify distinct sets of genes whose transcription patterns were highly responsive to different types of temperature conditions, some were common among the tissues and some were tissue-specific. Interestingly, we observed that the known temperature-responsive genes such as the heat-shock factor (HSF) family, were up-regulated not only in response to high temperatures, but some of its members were also likely involved in the cold response. By integrating the DNA-binding specificity information of the key temperature transcription factor (TF) HSFA1a, PIF4, and CBFs, we elucidated their distinct DNA-binding patterns to the target genes that showed different transcriptional responses. Taken together, we have comprehensively characterized the transcription patterns of temperature-responsive genes and provided directly testable hypotheses on the regulatory roles of key temperature TFs on the expression dynamics of their target genes.

Machine learning applications for transcription level and phenotype predictions.

Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of gene expression. With today's abundance of omic data and advances in high-throughput computing and machine learning (ML), we now have an unprecedented opportunity to uncover the missing links and molecular mechanisms that control gene expression and phenotypes. To empower molecular biologists and researchers in related fields to start using ML for in-depth analyses of their large-scale data, here we provide a summary of fundamental concepts of machine learning, and describe a wide range of research questions and scenarios in molecular biology where ML has been implemented. Due to the abundance of data, reproducibility, and genome-wide coverage, we focus on transcriptomics, and two ML tasks involving it: (a) predicting of transcriptomic profiles or transcription levels from genomic variations in DNA, and (b) predicting phenotypes of interest from transcriptomic profiles or transcription levels. Similar approaches can also be applied to more complex data such as those in multi-omic studies. We envisage that the concepts and examples described here will raise awareness and promote the application of ML among molecular biologists, and eventually help improve a framework for systematic design and predictions of gene expression and phenotypes for synthetic biology applications.

Antisense Oligonucleotide Induction of the hnRNPA1b Isoform Affects Pre-mRNA Splicing of SMN2 in SMA Type I Fibroblasts.

Spinal muscular atrophy (SMA) is a severe, debilitating neuromuscular condition characterised by loss of motor neurons and progressive muscle wasting. SMA is caused by a loss of expression of SMN1 that encodes the survival motor neuron (SMN) protein necessary for the survival of motor neurons. Restoration of SMN expression through increased inclusion of SMN2 exon 7 is known to ameliorate symptoms in SMA patients. As a consequence, regulation of pre-mRNA splicing of SMN2 could provide a potential molecular therapy for SMA. In this study, we explored if splice switching antisense oligonucleotides could redirect the splicing repressor hnRNPA1 to the hnRNPA1b isoform and restore SMN expression in fibroblasts from a type I SMA patient. Antisense oligonucleotides (AOs) were designed to promote exon 7b retention in the mature mRNA and induce the hnRNPA1b isoform. RT-PCR and western blot analysis were used to assess and monitor the efficiency of different AO combinations. A combination of AOs targeting multiple silencing motifs in hnRNPA1 pre-mRNA led to robust hnRNPA1b induction, which, in turn, significantly increased expression of full-length SMN (FL-SMN) protein. A combination of PMOs targeting the same motifs also strongly induced hnRNPA1b isoform, but surprisingly SMN2 exon 5 skipping was detected, and the PMO cocktail did not lead to a significant increase in expression of FL-SMN protein. We further performed RNA sequencing to assess the genome-wide effects of hnRNPA1b induction. Some 3244 genes were differentially expressed between the hnRNPA1b-induced and untreated SMA fibroblasts, which are functionally enriched in cell cycle and chromosome segregation processes. RT-PCR analysis demonstrated that expression of the master regulator of these enrichment pathways, MYBL2 and FOXM1B, were reduced in response to PMO treatment. These findings suggested that induction of hnRNPA1b can promote SMN protein expression, but not at sufficient levels to be clinically relevant.

The Evening Complex Establishes Repressive Chromatin Domains Via H2A.Z Deposition.

The Evening Complex (EC) is a core component of the Arabidopsis (Arabidopsis thaliana) circadian clock, which represses target gene expression at the end of the day and integrates temperature information to coordinate environmental and endogenous signals. Here we show that the EC induces repressive chromatin structure to regulate the evening transcriptome. The EC component ELF3 directly interacts with a protein from the SWI2/SNF2-RELATED (SWR1) complex to control deposition of H2A.Z-nucleosomes at the EC target genes. SWR1 components display circadian oscillation in gene expression with a peak at dusk. In turn, SWR1 is required for the circadian clockwork, as defects in SWR1 activity alter morning-expressed genes. The EC-SWR1 complex binds to the loci of the core clock genes PSEUDO-RESPONSE REGULATOR7 (PRR7) and PRR9 and catalyzes deposition of nucleosomes containing the histone variant H2A.Z coincident with the repression of these genes at dusk. This provides a mechanism by which the circadian clock temporally establishes repressive chromatin domains to shape oscillatory gene expression around dusk.

DNA sequence preferences of transcriptional activators correlate more strongly than repressors with nucleosomes.

Transcription factors (TFs) and histone octamers are two abundant classes of DNA binding proteins that coordinate the transcriptional program in cells. Detailed studies of individual TFs have shown that TFs bind to nucleosome-occluded DNA sequences and induce nucleosome disruption/repositioning, while recent global studies suggest this is not the only mechanism used by all TFs. We have analyzed to what extent the intrinsic DNA binding preferences of TFs and histones play a role in determining nucleosome occupancy, in addition to nonintrinsic factors such as the enzymatic activity of chromatin remodelers. The majority of TFs in budding yeast have an intrinsic sequence preference overlapping with nucleosomal histones. TFs with intrinsic DNA binding properties highly correlated with those of histones tend to be associated with gene activation and might compete with histones to bind to genomic DNA. Consistent with this, we show that activators induce more nucleosome disruption upon transcriptional activation than repressors.

Coordination of the Cell Wall Integrity and High-Osmolarity Glycerol Pathways in Response to Ethanol Stress in Saccharomyces cerevisiae.

During fermentation, a high ethanol concentration is a major stress that influences the vitality and viability of yeast cells, which in turn leads to a termination of the fermentation process. In this study, we show that the BCK1 and SLT2 genes encoding mitogen-activated protein kinase kinase kinase (MAPKKK) and mitogen-activated protein kinase (MAPK) of the cell wall integrity (CWI) pathway, respectively, are essential for ethanol tolerance, suggesting that the CWI pathway is involved in the response to ethanol-induced cell wall stress. Upon ethanol exposure, the CWI pathway induces the expression of specific cell wall-remodeling genes, including FKS2, CRH1, and PIR3 (encoding ß-1,3-glucan synthase, chitin transglycosylase, and O-glycosylated cell wall protein, respectively), which eventually leads to the remodeling of the cell wall structure. Our results revealed that in response to ethanol stress, the high-osmolarity glycerol (HOG) pathway plays a collaborative role with the CWI pathway in inducing cell wall remodeling via the upregulation of specific cell wall biosynthesis genes such as the CRH1 gene. Furthermore, the substantial expression of CWI-responsive genes is also triggered by external hyperosmolarity, suggesting that the adaptive changes in the cell wall are crucial for protecting yeast cells against not only cell wall stress but also osmotic stress. On the other hand, the cell wall stress-inducing agent calcofluor white has no effect on promoting the expression of GPD1, a major target gene of the HOG pathway. Collectively, these findings suggest that during ethanol stress, the CWI and HOG pathways collaboratively regulate the transcription of specific cell wall biosynthesis genes, thereby leading to adaptive changes in the cell wall.IMPORTANCE The budding yeast Saccharomyces cerevisiae has been widely used in industrial fermentations, including the production of alcoholic beverages and bioethanol. During fermentation, an increased ethanol concentration is the main stress that affects yeast metabolism and inhibits ethanol production. This work presents evidence that in response to ethanol stress, both CWI and HOG pathways cooperate to control the expression of cell wall-remodeling genes in order to build the adaptive strength of the cell wall. These findings will contribute to a better understanding of the molecular mechanisms underlying adaptive responses and tolerance of yeast to ethanol stress, which is essential for successful engineering of yeast strains for improved ethanol tolerance.

The G-Box Transcriptional Regulatory Code in Arabidopsis.

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein-ligand fragment interaction, pathways and SNPs.

Protein-ligand interaction analysis is an important step of drug design and protein engineering in order to predict the binding affinity and selectivity between ligands to the target proteins. To date, there are more than 100 000 structures available in the Protein Data Bank (PDB), of which ∼30% are protein-ligand (MW below 1000 Da) complexes. We have developed the integrative web server MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) with the aim of providing a user-friendly web interface to assist structural study and design of protein-ligand interactions. In brief, the server allows the users to input the chemical fragments and present all the unique molecular interactions to the target proteins with available three-dimensional structures in the PDB. The users can also link the ligands of interest to assess possible off-target proteins, human variants and pathway information using our all-in-one integrated tools. Taken together, we envisage that the server will facilitate and improve the study of protein-ligand interactions by allowing observation and comparison of ligand interactions with multiple proteins at the same time. (http://manoraa.org).

"Hit-and-run": Transcription factors get caught in the act.

A key challenge for understanding transcriptional regulation is being able to measure transcription factor (TF)-DNA binding events with sufficient spatial and temporal resolution; that is, when and where TFs occupy their cognate sites. A recent study by Para et al. has highlighted the dynamics underlying the activation of gene expression by a master regulator TF. This study provides concrete evidence for a long-standing hypothesis in biology, the "hit-and-run" mechanism, which was first proposed decades ago. That is, gene expression is dynamically controlled by a TF that transiently binds and activates a target gene, which might stay in a transcriptionally active state after the initial binding event has ended. Importantly, the experimental procedure introduced, TARGET, provides a useful way for identifying multiple target genes transiently bound by their regulators, which can be used in conjunction with other well-established methods to improve our understanding of transcriptional regulatory dynamics.

Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research.

With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians' point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world's major diseases.

Single-cell RNA sequencing reveals the expansion of circulating tissue-homing B cell subsets in secondary acute dengue viral infection.

The roles of antibodies secreted by subsets of B cells in dengue virus (DENV) infection have been extensively studied, yet, the contribution of tissue-homing B cells to antiviral immunity remains unclear. In this study, we performed a comprehensive analysis of B cell subpopulations in peripheral blood samples from DENV-infected patients using single-cell RNA-sequencing (scRNA-seq) datasets and flow cytometry. We showed that plasma cells (PCs) and plasmablasts (PBs) were the predominant B cell populations during the acute phase of secondary natural DENV infection, but not in convalescent phase nor in healthy controls. Interestingly, these cells expressed proliferation, adhesion, and tissue-homing genes, including SELPLG, a homing marker of the skin, the initial infected site of DENV. Flow cytometry analysis confirmed a significant upregulation of cell surface expression of a cutaneous lymphocyte-associated antigen (CLA) encoded by SELPLG in PCs and PBs, compared to naive and memory B cells from the same patients. The analysis of an independent single-cell B-cell receptor sequencing (scBCR-seq) dataset of DENV-infected patients revealed that the peripheral blood PCs and PBs exhibited the highest clonal expansion in secondary DENV infection compared to other B cell subsets. These clonally expanded cells also expressed the highest levels of tissue-homing genes, including SELPLG. In addition, by utilizing a public scRNA-seq dataset of SARS-CoV2 infection, we demonstrated the upregulation of several tissue-homing genes in PCs and PBs. Our study provides evidence for the potential roles of tissue-homing B cell subsets in the context of immune responses against viral infections in humans.

Comparative quantitative trait loci analysis framework reveals relationships between salt stress responsive phenotypes and pathways.

Soil salinity is a complex abiotic stress that involves several biological pathways. Hence, focusing on a specific or a few salt-tolerant phenotypes is unlikely to provide comprehensive insights into the intricate and interwinding mechanisms that regulate salt responsiveness. In this study, we develop a heuristic framework for systematically integrating and comprehensively evaluating quantitative trait loci (QTL) analyses from multiple stress-related traits obtained by different studies. Making use of a combined set of 46 salinity-related traits from three independent studies that were based on the same chromosome segment substitution line (CSSL) population of rice (Oryza sativa), we demonstrate how our approach can address technical biases and limitations from different QTL studies and calling methods. This allows us to compile a comprehensive list of trait-specific and multi-trait QTLs, as well as salinity-related candidate genes. In doing so, we discover several novel relationships between traits that demonstrate similar trends of phenotype scores across the CSSLs, as well as the similarities between genomic locations that the traits were mapped to. Finally, we experimentally validate our findings by expression analyses and functional validations of several selected candidate genes from multiple pathways in rice and Arabidopsis orthologous genes, including OsKS7 (ENT-KAURENE SYNTHASE 7), OsNUC1 (NUCLEOLIN 1) and OsFRO1 (FERRIC REDUCTASE OXIDASE 1) to name a few. This work not only introduces a novel approach for conducting comparative analyses of multiple QTLs, but also provides a list of candidate genes and testable hypotheses for salinity-related mechanisms across several biological pathways.

Lineage-specific expansion of DNA-binding transcription factor families.

DNA-binding domains (DBDs) are essential components of sequence-specific transcription factors (TFs). We have investigated the distribution of all known DBDs in more than 500 completely sequenced genomes from the three major superkingdoms (Bacteria, Archaea and Eukaryota) and documented conserved and specific DBD occurrence in diverse taxonomic lineages. By combining DBD occurrence in different species with taxonomic information, we have developed an automatic method for inferring the origins of DBD families and their specific combinations with other protein families in TFs. We found only three out of 131 (2%) DBD families shared by the three superkingdoms.

RpoS and oxidative stress conditions regulate succinyl-CoA: 3-ketoacid-coenzyme A transferase (SCOT) expression in Burkholderia pseudomallei.

Burkholderia pseudomallei, a pathogenic gram-negative bacterium, causes the severe human disease melioidosis. This organism can survive in eukaryotic host cells by escaping reactive oxygen species via the regulation of stress responsive sigma factors, including RpoS. In B. pseudomallei, RpoS has been reported to play a role in the oxidative stress response through enhanced activity of OxyR and catalase. In this study, the RpoS dependent oxidative stress responsive system was further characterized using comparative proteomic analysis. The proteomic profiles of wild-type B. pseudomallei following exposure to H2 O2 and between wild-type and the rpoS mutant strains were analyzed. Using stringent criteria, 13 oxidative responsive proteins, eight of which are regulated by RpoS, were identified with high confidence. It was observed that ScoA, a subunit of the SCOT enzyme not previously shown to be involved directly in the oxidative stress response, is significantly down-regulated after hydrogen peroxide treatment. ScoA and ScoB have been predicted to be organized in a single operon using computational methods: in this study it was confirmed by RT-PCR that these genes are indeed co-transcribed as a single mRNA. The present study is the first to report a role for RpoS in the down-regulation of SCOT expression in response to oxidative stress in B. pseudomallei.

Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types.

Here, we present a computational approach for investigating highly variable genes (HVGs) associated with biological pathways of interest, across multiple time points and cell types in single-cell RNA-sequencing (scRNA-seq) data. Using public dengue virus and COVID-19 datasets, we describe steps for using the framework to characterize the dynamic expression levels of HVGs related to common and cell-type-specific biological pathways over multiple immune cell types. For complete details on the use and execution of this protocol, please refer to Arora et al.1.

Characterizing Different Modes of Interplay Between Rap1 and H3 Using Inducible H3-depletion Yeast.

Histones and transcription factors (TFs) are two important DNA-binding proteins that interact, compete, and together regulate transcriptional processes in response to diverse internal and external stimuli. Condition-specific depletion of histones in Saccharomyces cerevisiae using a galactose-inducible H3 promoter provides a suitable framework for examining transcriptional alteration resulting from reduced nucleosome content. However, the effect on DNA binding activities of TFs is yet to be fully explored. In this work, we combine ChIP-seq of H3 with RNA-seq to elucidate the genome-scale relationships between H3 occupancy patterns and transcriptional dynamics before and after global H3 depletion. ChIP-seq of Rap1 is also conducted in the H3-depletion and control treatments, to investigate the interplay between this master regulator TF and nucleosomal H3, and to explore the impact on diverse transcriptional responses of different groups of target genes and functions. Ultimately, we propose a working model and testable hypotheses regarding the impact of global and local H3 depletion on transcriptional changes. We also demonstrate different potential modes of interaction between Rap1 and H3, which sheds light on the potential multifunctional regulatory capabilities of Rap1 and potentially other pioneer factors.

Predicting the impact of sequence motifs on gene regulation using single-cell data.

The binding of transcription factors at proximal promoters and distal enhancers is central to gene regulation. Identifying regulatory motifs and quantifying their impact on expression remains challenging. Using a convolutional neural network trained on single-cell data, we infer putative regulatory motifs and cell type-specific importance. Our model, scover, explains 29% of the variance in gene expression in multiple mouse tissues. Applying scover to distal enhancers identified using scATAC-seq from the developing human brain, we identify cell type-specific motif activities in distal enhancers. Scover can identify regulatory motifs and their importance from single-cell data where all parameters and outputs are easily interpretable.

RNA sequencing reveals two major classes of gene expression levels in metazoan cells.

The expression level of a gene is often used as a proxy for determining whether the protein or RNA product is functional in a cell or tissue. Therefore, it is of fundamental importance to understand the global distribution of gene expression levels, and to be able to interpret it mechanistically and functionally. Here we use RNA sequencing (RNA-seq) of mouse Th2 cells, coupled with a range of other techniques, to show that all genes can be separated, based on their expression abundance, into two distinct groups: one group comprised of lowly expressed and putatively non-functional mRNAs, and the other of highly expressed mRNAs with active chromatin marks at their promoters. These observations are confirmed in many other microarray and RNA-seq data sets of metazoan cell types.

Construction of a large extracellular protein interaction network and its resolution by spatiotemporal expression profiling.

Extracellular interactions involving both secreted and membrane-tethered receptor proteins are essential to initiate signaling pathways that orchestrate cellular behaviors within biological systems. Because of the biochemical properties of these proteins and their interactions, identifying novel extracellular interactions remains experimentally challenging. To address this, we have recently developed an assay, AVEXIS (avidity-based extracellular interaction screen) to detect low affinity extracellular interactions on a large scale and have begun to construct interaction networks between zebrafish receptors belonging to the immunoglobulin and leucine-rich repeat protein families to identify novel signaling pathways important for early development. Here, we expanded our zebrafish protein library to include other domain families and many more secreted proteins and performed our largest screen to date totaling 16,544 potential unique interactions. We report 111 interactions of which 96 are novel and include the first documented extracellular ligands for 15 proteins. By including 77 interactions from previous screens, we assembled an expanded network of 188 extracellular interactions between 92 proteins and used it to show that secreted proteins have twice as many interaction partners as membrane-tethered receptors and that the connectivity of the extracellular network behaves as a power law. To try to understand the functional role of these interactions, we determined new expression patterns for 164 genes within our clone library by using whole embryo in situ hybridization at five key stages of zebrafish embryonic development. These expression data were integrated with the binding network to reveal where each interaction was likely to function within the embryo and were used to resolve the static interaction network into dynamic tissue- and stage-specific subnetworks within the developing zebrafish embryo. All these data were organized into a freely accessible on-line database called ARNIE (AVEXIS Receptor Network with Integrated Expression; www.sanger.ac.uk/arnie) and provide a valuable resource of new extracellular signaling interactions for developmental biology.