High-throughput Expression Profiling Research Articles

Investigating the potential effects of selective histone deacetylase 6 inhibitor ACY1215 on infarct size in rats with cardiac ischemia-reperfusion injury

BackgroundDespite the fact that histone deacetylase (HDAC) inhibitors have been tested to treat various cardiovascular diseases, the effects of selective HDAC6 inhibitor ACY1215 on infarct size during cardiac ischemia-reperfusion (IR) injury still remain unknown. In the present study we aimed to investigate the effects of ACY1215 on infarct size in rats with cardiac IR injury, as well as to examine the association between HDAC6 inhibitors and the gene expression of hypoxia inducible factor-1α (HIF-1α), a key regulator of cellular responses to hypoxia.MethodsBy using computational analysis of high-throughput expression profiling dataset, the association between HDAC inhibitors (pan-HDAC inhibitors panobinostat and vorinostat, and HDAC6 inhibitor ISOX) and their effects on HIF-1α gene-expression were evaluated. The male Wistar rats treated with ligation of left coronary artery followed by reperfusion were used as a cardiac IR model. ACY1215 (50 mg/kg), pan-HDAC inhibitor MPT0E028 (25 mg/kg), and vehicle were intraperitoneally injected within 5 min before reperfusion. The infarct size in rat myocardium was determined by 2,3,5-triphenyltetrazolium chloride staining. The serum levels of transforming growth factor-β (TGF-β) and C-reactive protein (CRP) were also determined.ResultsThe high-throughput gene expression assay showed that treatment of ISOX was associated with a more decreased gene expression of HIF-1α than that of panobinostat and vorinostat. Compared to control rats, ACY1215-treated rats had a smaller infarct size (49.75 ± 9.36% vs. 19.22 ± 1.70%, p < 0.05), while MPT0E028-treated rats had a similar infarct size to control rats. ACY-1215- and MPT0E028-treated rats had a trend in decreased serum TGF-β levels, but not statistically significant. ACY1215-treated rats also had higher serum CRP levels compared to control rats (641.6 μg/mL vs. 961.37 ± 64.94 μg/mL, p < 0.05).ConclusionsOur research indicated that HDAC6 inhibition by ACY1215 might reduce infarct size in rats with cardiac IR injury possibly through modulating HIF-1α expression. TGF-β and CRP should be useful biomarkers to monitor the use of ACY1215 in cardiac IR injury.

Multiplexed Quantitative In Situ Hybridization for Mammalian or Bacterial Cells in Suspension: qHCR Flow Cytometry (v3.0).

In situ hybridization based on the mechanism of hybridization chain reaction (HCR) enables high-throughput expression profiling of mammalian or bacterial cells via flow cytometry. Third-generation in situ HCR (v3.0) provides automatic background suppression throughout the protocol, dramatically enhancing performance and ease of use. In situ HCR v3.0 supports analog mRNA relative quantitation via qHCR flow cytometry. Here, we provide protocols for multiplexed qHCR flow cytometry for mammalian or bacterial cells in suspension.

Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis.

Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.

A novel risk score model for stomach adenocarcinoma based on the expression levels of 10 genes.

Stomach adenocarcinoma (STAD) accounts for 95% of cases of malignant gastric cancer, which is the third leading cause of cancer-associated mortality worldwide. The pathogenesis and effective diagnosis of STAD have become popular topics for research in the previous decade. In the present study, high-throughput RNA sequencing expression profiles and clinical data from patients with STAD were obtained from The Cancer Genome Atlas database and were used as a training dataset to screen differentially expressed genes (DEGs). Prognostic DEGs were identified using univariate Cox regression analysis and were further screened by the least absolute shrinkage and selection operator regularization regression algorithm. The resulting genes were used to construct a risk score model, the validation and effectiveness evaluation of which were performed on an independent dataset downloaded from the Gene Expression Omnibus database. Stratified and functional pathway (gene set enrichment) analyses were performed on groups with different estimated prognosis. A total of 92 genes significantly associated with STAD prognosis were obtained by univariate Cox regression analysis, and 10 prognosis-associated DEGs; hemoglobin b, chromosome 4 open reading frame 48, Dickkopf WNT signaling pathway inhibitor 1, coagulation factor V, serpin family E member 1, transmembrane protein 200A, NADPH oxidase organizer 1, C-X-C motif chemokine ligand 3, mannosidase endo-α-like and tripartite motif-containing 31; were selected for the development of the risk score model. The reliability of this prognostic method was verified using a validation set, and the results of multivariate Cox analysis indicated that the risk score may serve as an independent prognostic factor. In functional DEG analysis, eight Kyoto Encyclopedia of Genes and Genomes pathways were identified to be significantly associated with STAD risk factors. Thus, the 10-gene risk score model established in the present study was regarded as credible. This risk assessment tool may help identify patients with a high risk of STAD, and the proposed prognostic mRNAs may be useful in elucidating STAD pathogenesis.

MS/MS in silico subtraction-based proteomic profiling as an approach to facilitate disease gene discovery: application to lens development and cataract.

While the bioinformatics resource-tool iSyTE (integrated Systems Tool for Eye gene discovery) effectively identifies human cataract-associated genes, it is currently based on just transcriptome data, and thus, it is necessary to include protein-level information to gain greater confidence in gene prioritization. Here, we expand iSyTE through development of a novel proteome-based resource on the lens and demonstrate its utility in cataract gene discovery. We applied high-throughput tandem mass spectrometry (MS/MS) to generate a global protein expression profile of mouse lens at embryonic day (E)14.5, which identified 2371 lens-expressed proteins. A major challenge of high-throughput expression profiling is identification of high-priority candidates among the thousands of expressed proteins. To address this problem, we generated new MS/MS proteome data on mouse whole embryonic body (WB). WB proteome was then used as a reference dataset for performing "in silico WB-subtraction" comparative analysis with the lens proteome, which effectively identified 422 proteins with lens-enriched expression at ≥ 2.5 average spectral counts, ≥ 2.0 fold enrichment (FDR < 0.01) cut-off. These top 20% candidates represent a rich pool of high-priority proteins in the lens including known human cataract-linked genes and many new potential regulators of lens development and homeostasis. This rich information is made publicly accessible through iSyTE (https://research.bioinformatics.udel.edu/iSyTE/), which enables user-friendly visualization of promising candidates, thus making iSyTE a comprehensive tool for cataract gene discovery.

High Throughput Immunophenotyping and Expression Profiling at Single Cell Level Reveal BCR-ABL1 Dependent Surface Markers of Chronic Myeloid Leukemia Stem Cells

BACKGROUND: Bcr-abl1 oncogene targeted treatment with tyrosine kinase inhibitors (TKI) showed an impressive efficacy against proliferating chronic myeloid leukemia (CML) cells. However, rapid relapses in more than half of CML patients after discontinuation of the treatment suggest a presence of quiescent leukemic stem cells inherently resistant to BCR-ABL1 inhibition. Understanding the heterogeneity of CML stem cell compartment is crucial for preventing the treatment failure. Specificity of already established leukemic stem cell (LSC) markers has been tested mainly in bulk CD34+CD38- populations at diagnosis. Phenotypes and molecular signatures of therapy resistant BCR ABL1 positive stem cells is however yet to be established. AIMS: Identification of BCR-ABL1 dependent LSC markers at single cell level by direct comparison their surface and transcript expression with the levels and the presence of BCR-ABL1 transcript at diagnosis and after administration of TKI treatment. METHODS: Total number of 375 cells were obtained from bone marrow and peripheral blood of 4 chronic phase CML patients. Cells were collected prior any treatment and three months after TKI treatment initiation. Normal bone marrow cells and BCR-ABL1 positive K562 cell line were used as controls. Indexed immuno-phenotyping and sorting of CD34+CD38- single cells was performed using a panel of 11 specific surface markers. Collected single cells were lysed and cDNA was enriched for 11 targets using 22 cycle pre-amplification. Expression profiling was carried on SmartChip real-time PCR system (Takara Bio) detecting following genes: BCR-ABL1, CD26, CD25, IL1-Rap, CD56, CD90, CD93, CD69, KI67, and control genes GUS and HPRT. Unsupervised clustering was performed using principal component analysis (PCA). Correlations were measured by Spearman rank method. RESULTS: At diagnosis, majority of BCR-ABL1+ C34+CD38- stem cells co-express IL1-Rap, CD26, and CD69 on their surface (88%, 82%, 78% overlap). Only 56% of BCR-ABL1+ cells positive for aforementioned markers co-express CD25, 28% CD93 and 16% CD56. The expression of these markers could also be detected in 4-11% of BCR-ABL1- cell, although this could be technical inaccuracy caused by the single cell profiling. CD90 marker did not show any correlation with BCR-ABL1 expression. At transcript level the expression of IL-1Rap, CD26, CD25 and CD56 was observed in 62%, 52% 45% and 16% BCR-ABL1+ cells, and up to 7% of BCR-ABL1- cells. CD69 expression was observed in 90% of BCR-ABL+ cells at transcript level, but also in 71% BCR-ABL- cells. BCR-ABL1 independent expression was observed for cKIT. (60% vs. 76 % in positive vs negative). Finally proliferation marker KI67 was expressed only in 6% of the BCR-ABL1+ cells. PCA analysis divided cells into several distinct clusters with BCR-ABL1 as the main contributor, and cKIT, CD69 and CD26, IL-1RAP as other significant factors. Interestingly BCR-ABL1+ cells collected during TKI treatment showed persistent surface expression of IL-1Rap and CD26, while CD56, CD69 and CD93 were only on part of the BCR-ABL1+ cells. CD25 was significantly deregulated during TKI treatment. CONCLUSION: At diagnosis up to 80% of LSC co-express 3 specific surface markers - IL-1RAP, CD26 and CD69. Variable portion of LSC co-express additional markers such are CD25, CD56 and CD93. During TKI treatment the surface expression of majority of markers is decreased, where the best correlated LSC marker is IL-1Rap, followed by CD26 and CD69. CD56 marker seems to persist in the same proportion of cells while CD25 disappears. cKIT is highly expressed in normal BM and HSC from CML patients, but also in some LSC. CD34+CD38- cells show non-proliferating phenotype. Disclosures Mayer: AOP Orphan Pharmaceuticals AG: Research Funding.

FOXP1 circular RNA sustains mesenchymal stem cell identity via microRNA inhibition.

Stem cell identity and plasticity are controlled by master regulatory genes and complex circuits also involving non-coding RNAs. Circular RNAs (circRNAs) are a class of RNAs generated from protein-coding genes by backsplicing, resulting in stable RNA structures devoid of free 5’ and 3’ ends. Little is known of the mechanisms of action of circRNAs, let alone in stem cell biology. In this study, for the first time, we determined that a circRNA controls mesenchymal stem cell (MSC) identity and differentiation. High-throughput MSC expression profiling from different tissues revealed a large number of expressed circRNAs. Among those, circFOXP1 was enriched in MSCs compared to differentiated mesodermal derivatives. Silencing of circFOXP1 dramatically impaired MSC differentiation in culture and in vivo. Furthermore, we demonstrated a direct interaction between circFOXP1 and miR-17–3p/miR-127–5p, which results in the modulation of non-canonical Wnt and EGFR pathways. Finally, we addressed the interplay between canonical and non-canonical Wnt pathways. Reprogramming to pluripotency of MSCs reduced circFOXP1 and non-canonical Wnt, whereas canonical Wnt was boosted. The opposing effect was observed during generation of MSCs from human pluripotent stem cells. Our results provide unprecedented evidence for a regulatory role for circFOXP1 as a gatekeeper of pivotal stem cell molecular networks.

An Integrated Regulatory Network Based on Comprehensive Analysis of mRNA Expression, Gene Methylation and Expression of Long Non-coding RNAs (lncRNAs) in Myelodysplastic Syndromes.

Myelodysplastic syndromes (MDS) are a heterogeneous group of disorders characterized by ineffective hematopoiesis, defective differentiation of hematopoietic precursors, and expansion of the abnormal clones. The prevalence of MDS has raised great concerns worldwide, but its pathogenetic mechanisms remain elusive. To provide insights on novel biomarkers for the diagnosis and therapy of MDS, we performed high-throughput genome-wide mRNA expression profiling, DNA methylation analysis, and long non-coding RNAs (lncRNA) analysis on bone marrows from four MDS patients and four age-matched healthy controls. We identified 1,937 differentially expressed genes (DEGs), 515 methylated genes, and 214 lncRNA that showed statistically significant differences. As the most significant module-related DEGs, TCL1A, PTGS2, and MME were revealed to be enriched in regulation of cell differentiation and cell death pathways. In addition, the GeneGo pathway maps identified by top DEGs were shown to converge on cancer, immunoregulation, apoptosis and regulation of actin cytoskeleton, most of which are known contributors in MDS etiology and pathogenesis. Notably, as potential biomarkers for diagnosis of MDS, four specific genes (ABAT, FADD, DAPP1, and SMPD3) were further subjected to detailed pathway analysis. Our integrative analysis on mRNA expression, gene methylation and lncRNAs profiling facilitates further understanding of the pathogenesis of MDS, and may promote the diagnosis and novel therapeutics for this disease.

SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles.

Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering. Clusters are often assigned to different cell types based on the enriched canonical markers. However, this process is inefficient and arbitrary. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities as soon as the single-cell expression profile is input. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of the model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.

Circular RNA hsa_circ_0007059 indicates prognosis and influences malignant behavior via AKT/mTOR in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC), the most common oral cancer, damages oral epithelial cells after the accumulation of multiple genetic mutations. Although emerging evidence supports the key role of circular RNAs (circRNAs) in various malignancies, the clinical value and function of circRNAs in OSCC remain unclear. In this study, patients with OSCC (n = 8) and controls ( n = 8) were compared using high‐throughput sequencing and microarray circRNA expression profiles. The circRNA hsa_circ_0007059 was downregulated in OSCC. Subsequently, hsa_circ_0007059 levels in OSCC tissues and cell lines were assessed by quantitative reverse‐transcription chain reaction. Loss‐of‐function and gain‐of‐function experiments were performed to determine whether hsa_circ_0007059 affects malignant behavior in SCC15 and CAL27 cells. Importantly, hsa_circ_0007059 upregulation suppressed cell growth, migration, and invasion, facilitating apoptosis of these cells. Furthermore, nude mouse tumor formation was assessed to validate the tumor‐suppressive role of hsa_circ_0007059 in vivo. Finally, hsa_circ_0007059 was determined to alter cell growth via AKT/mTOR signaling, representing a potential prognostic/therapeutic target for OSCC.

Differences in microRNA expression in breast cancer between women of African and European ancestry

Breast cancer is a heterogeneous disease, characterized by molecularly and phenotypically distinct tumor subtypes, linked to disparate clinical outcomes. American women of African ancestry (AA) are more likely than those of European ancestry (EA) to be diagnosed with aggressive, estrogen receptor negative (ER-) or triple negative breast cancer, and to die of this disease. However, the underlying causes of AA predisposition to ER-/triple negative breast cancer are still largely unknown. In this study, we performed high-throughput whole-genome miRNA expression profiling in breast tissue samples from both AA and EA women. A number of differentially expressed miRNAs, i.e., DEmiRs defined as >2-fold change in expression and false discovery rate <0.05, were identified as up- or downregulated by tumor ER status or by ancestry. We found that among 102 ER-subtype-related DEmiRs identified in breast tumors, the majority of these DEmiRs were race specific, with only 23 DEmiRs shared in tumors from both AAs and EAs; this finding indicates that there are unique subsets of miRNAs differentially expressed between ER- and ER positive tumors within AAs versus EAs. Our overall results support the notion that miRNA expression patterns may differ not only by tumor subtype but by ancestry, indicating differences in tumor biology and heterogeneity of breast cancer between AAs and EAs. These results will provide the basis for further functional analysis to elucidate biological differences between AAs and EAs and to help develop targeted treatment strategies to reduce disparities in breast cancer.

Genomics-Assisted Identification and Characterization of the Genetic Variants Underlying Differential Nitrogen Use Efficiencies in Allotetraploid Rapeseed Genotypes.

Nitrogen (N) is a non-mineral macronutrient essential for plant growth and development. Oilseed rape (AnAnCnCn, 2n = 4x = 38) has a high requirement for N nutrients whereas showing the lowest N use efficiency (NUE) among crops. The mechanisms underlying NUE regulation in Brassica napus remain unclear because of genome complexity. In this study, we performed high-depth and -coverage whole-genome re-sequencing (WGS) of an N-efficient (higher NUE) genotype “XY15” and an N-inefficient (lower NUE) genotype “814” of rapeseed. More than 687 million 150-bp paired-end reads were generated, which provided about 93% coverage and 50× depth of the rapeseed genome. Applying stringent parameters, we identified a total of 1,449,157 single-nucleotide polymorphisms (SNPs), 335,228 InDels, 175,602 structure variations (SVs) and 86,280 copy number variations (CNVs) between the N-efficient and -inefficient genotypes. The largest proportion of various DNA polymorphisms occurred in the inter-genic regions. Unlike CNVs, the SNP/InDel and SV polymorphisms showed variation bias of the An and Cn subgenomes, respectively. Gene ontology analysis showed the genetic variants were mapped onto the genes involving N compound transport and ATPase complex metabolism, but not including N assimilation-related genes. On basis of identification of N-starvation responsive genes through high-throughput expression profiling, we also mapped these variants onto some key NUE-regulating genes, and validated their significantly differential expression between the N-efficient and -inefficient genotypes through qRT-PCR assays. Our data provide genome-wide high resolution DNA variants underlying NUE divergence in allotetraploid rapeseed genotypes, which would expedite the effective identification and functional validation of key NUE-regulating genes through genomics-assisted improvement of crop nutrient efficiency.

Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust.

In situ hybridization based on the mechanism of the hybridization chain reaction (HCR) has addressed multi-decade challenges that impeded imaging of mRNA expression in diverse organisms, offering a unique combination of multiplexing, quantitation, sensitivity, resolution and versatility. Here, with third-generation in situ HCR, we augment these capabilities using probes and amplifiers that combine to provide automatic background suppression throughout the protocol, ensuring that reagents will not generate amplified background even if they bind non-specifically within the sample. Automatic background suppression dramatically enhances performance and robustness, combining the benefits of a higher signal-to-background ratio with the convenience of using unoptimized probe sets for new targets and organisms. In situ HCR v3.0 enables three multiplexed quantitative analysis modes: (1) qHCR imaging - analog mRNA relative quantitation with subcellular resolution in the anatomical context of whole-mount vertebrate embryos; (2) qHCR flow cytometry - analog mRNA relative quantitation for high-throughput expression profiling of mammalian and bacterial cells; and (3) dHCR imaging - digital mRNA absolute quantitation via single-molecule imaging in thick autofluorescent samples.

Differentially expressed circulating microRNAs in the development of acute diabetic Charcot foot.

Aim:Charcot foot (CF) is a rare complication of Type 2 diabetes (T2D).Materials & methods:We assessed circulating miRNAs in 17 patients with T2D and acute CF (G1), 17 patients with T2D (G2) and equivalent neuropathy and 17 patients with T2D without neuropathy (G3) using the high-throughput miRNA expression profiling.Results:51 significantly deregulated miRNAs were identified in G1 versus G2, 37 in G1 versus G3 and 64 in G2 versus G3. Furthermore, we demonstrated that 16 miRNAs differentially expressed between G1 versus G2 could be involved in osteoclastic differentiation. Among them, eight are key factors involved in CF pathophysiology.Conclusion:Our data reveal that CF patients exhibit an altered expression profile of circulating miRNAs.

LIMD1 is induced by and required for LMP1 signaling, and protects EBV-transformed cells from DNA damage-induced cell death.

LIMD1 (LIM domain-containing protein 1) is considered as a tumor suppressor, being deregulated in many cancers to include hematological malignancies; however, very little is known about the underlying mechanisms of its deregulation and its roles in carcinogenesis. Epstein-Barr Virus (EBV) is associated with a panel of malignancies of lymphocytic and epithelial origin. Using high throughput expression profiling, we have previously identified LIMD1 as a common marker associated with the oncogenic transcription factor IRF4 in EBV-related lymphomas and other hematological malignancies. In this study, we have identified potential conserved IRF4- and NFκB-binding motifs in the LIMD1 gene promoter, and both are demonstrated functional by promoter-reporter assays. We further show that LIMD1 is partially upregulated by EBV latent membrane protein 1 (LMP1) via IRF4 and NFκB in EBV latency. As to its role in the setting of EBV latent infection, we show that LIMD1 interacts with TRAF6, a crucial mediator of LMP1 signal transduction. Importantly, LIMD1 depletion impairs LMP1 signaling and functions, potentiates ionomycin-induced DNA damage and apoptosis, and inhibits p62-mediated selective autophagy. Taken together, these results show that LIMD1 is upregulated in EBV latency and plays an oncogenic role rather than that of a tumor suppressor. Our findings have identified LIMD1 as a novel player in EBV latency and oncogenesis, and open a novel research avenue, in which LIMD1 and p62 play crucial roles in linking DNA damage response (DDR), apoptosis, and autophagy and their potential interplay during viral oncogenesis.

Massively parallel nanowell-based single-cell gene expression profiling

BackgroundTechnological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable.ResultsHere we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells.ConclusionsOverall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples.

Spatially resolved transcriptome profiling in model plant species.

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.

High‐Throughput Natural Products Discovery in Fungi Using FAC‐MS Technology

Ascomycete fungal genomes typically each contain >50 different secondary metabolite biosynthetic gene clusters (BGCs). With an estimated pool of >500,000 ascomycete fungal species on earth, the corresponding chemical potential that could be harnessed for human technological challenges such as disease and agriculture is truly massive. However, the vast majority of fungal-encoded chemical space is uncharted and difficult to access due to difficulties culturing and genetically manipulating most fungi. We report a high-throughput heterologous expression and metabolomic profiling technology using untargeted liquid chromatography-mass spectrometry (LC-MS) with ultrahigh mass accuracy to systematically identify the secondary metabolite (SM) products of heterologously expressed fungal BGCs. This platform uses fungal artificial chromosomes (FACs) to capture full-length BGCs derived from unbiased large-insert libraries of genomic DNA of Aspergillus terreus, A. aculeatus, and A. wenti (Fig. A). Host A. nidulans strains transformed with FACs (Fig. B) are then screened by LC-MS (Fig. C), and FAC-encoded products are identified by a robust scoring system to identify spectral features most likely associated with each FAC (Fig. D). Deletions of specific backbone and tailoring genes within FACs (achieved through facile E. coli genetics) then empirically validate assignment of metabolites to BGCs and facilitate analyses of biosynthesis (Fig. E). We use this “FAC-MS” platform to screen 56 FACs and report detection of 15 completely novel secondary metabolites from 56 FACs, a ~27% “hit rate”, including an A. terreus FAC encoding three distinct metabolites identified as a novel macrolactone, a sesterterpenoid, and the orphan benzodiazepine benzomalvin A, which is 300-fold overexpressed by the FAC-strain. Deletants of benzomalvin A backbone and tailoring genes allow us to propose and test the first biosynthetic model for this molecule, as well as demonstrate the readiness with which such studies can be carried out using the FAC-MS technology. Wide application of the FAC-MS pipeline should have a major impact on fungal natural products research in the mid-term future by greatly increasing the scale and rate at which fungal secondary metabolites can be systematically unearthed and introduced into pharmaceutical and agricultural screening campaigns. Support or Funding InformationThis work was supported in part by an SBIR award from the National Institute of Allergy and Infectious Diseases at the National Institutes of Health under grant R44AI094885 to C.C.W. at Intact Genomics, Inc. and to J.W.B and N.L.K. N.L.K. also acknowledges AT009143 for partial support of this work. A) Fungal artificial chromosomes (FACs) capture full-length biosynthetic gene clusters (BGCs) derived from unbiased large-insert libraries of genomic DNA of Aspergillus terreus, A. aculeatus, and A. wenti. B and C) Host A. nidulans strains transformed with FACs are then screened by LC-MS. D) FAC-encoded products are identified by a robust scoring system. E) Deletions of backbone genes in FACs empirically validate assignment of metabolites to BGCs and dissect biosyntheses.

Representing high throughput expression profiles via perturbation barcodes reveals compound targets.

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound’s high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.

Dissecting the Transcriptional Patterns of Social Dominance across Teleosts.

In many species, under varying ecological conditions, social interactions among individuals result in the formation of dominance hierarchies. Despite general similarities, there are robust differences among dominance hierarchies across species, populations, environments, life stages, sexes, and individuals. Understanding the proximate mechanisms underlying the variation is an important step toward understanding the evolution of social behavior. However, physiological changes associated with dominance, such as gonadal maturation and somatic growth, often complicate efforts to identify the specific underlying mechanisms. Traditional gene expression analyses are useful for generating candidate gene lists, but are biased by choice of significance cut-offs and difficult to use for between-study comparisons. In contrast, complementary analysis tools allow one to both test a priori hypotheses and generate new hypotheses. Here we employ a meta-analysis of high-throughput expression profiling experiments to investigate the gene expression patterns that underlie mechanisms and evolution of behavioral social phenotypes. Specifically, we use a collection of datasets on social dominance in fish across social contexts, sex, and species. Using experimental manipulation to produce female dominance hierarchies in the cichlid Astatotilapia burtoni, heralded as a genomic model of social dominance, we generate gene lists, and assess molecular gene modules. In the dominant female gene expression profile, we demonstrate a strong pattern of up-regulation of genes previously identified as having male-biased expression and furthermore, compare expression biases between male and female dominance phenotypes. Using a threshold-free approach to identify correlation throughout ranked gene lists, we query previously published datasets associated with maternal behavior, alternative reproductive tactics, cooperative breeding, and sex-role reversal to describe correlations among these various neural gene expression profiles associated with different instances of social dominance. These complementary approaches capitalize on the high-throughput gene expression profiling from similar behavioral phenotypes in order to address the mechanisms associated with social dominance behavioral phenotypes.