Light-focusing human micro-lenses generated from pluripotent stem cells model lens development and drug-induced cataract in vitro.

Cataracts cause vision loss and blindness by impairing the ability of the ocular lens to focus light onto the retina. Various cataract risk factors have been identified, including drug treatments, age, smoking and diabetes. However, the molecular events responsible for these different forms of cataract are ill-defined, and the advent of modern cataract surgery in the 1960s virtually eliminated access to human lenses for research. Here, we demonstrate large-scale production of light-focusing human micro-lenses from spheroidal masses of human lens epithelial cells purified from differentiating pluripotent stem cells. The purified lens cells and micro-lenses display similar morphology, cellular arrangement, mRNA expression and protein expression to human lens cells and lenses. Exposing the micro-lenses to the emergent cystic fibrosis drug Vx-770 reduces micro-lens transparency and focusing ability. These human micro-lenses provide a powerful and large-scale platform for defining molecular disease mechanisms caused by cataract risk factors, for anti-cataract drug screening and for clinically relevant toxicity assays.

iSyTE 2.0: a database for expression-based gene discovery in the eye.

Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.

Identification of satellite cells from anole lizard skeletal muscle and demonstration of expanded musculoskeletal potential.

The lizards are evolutionarily the closest vertebrates to humans that demonstrate the ability to regenerate entire appendages containing cartilage, muscle, skin, and nervous tissue. We previously isolated PAX7-positive cells from muscle of the green anole lizard, Anolis carolinensis, that can differentiate into multinucleated myotubes and express the muscle structural protein, myosin heavy chain. Studying gene expression in these satellite/progenitor cell populations from A. carolinensis can provide insight into the mechanisms regulating tissue regeneration. We generated a transcriptome from proliferating lizard myoprogenitor cells and compared them to transcriptomes from the mouse and human tissues from the ENCODE project using XGSA, a statistical method for cross-species gene set analysis. These analyses determined that the lizard progenitor cell transcriptome was most similar to mammalian satellite cells. Further examination of specific GO categories of genes demonstrated that among genes with the highest level of expression in lizard satellite cells were an increased number of genetic regulators of chondrogenesis, as compared to mouse satellite cells. In micromass culture, lizard PAX7-positive cells formed Alcian blue and collagen 2a1 positive nodules, without the addition of exogenous morphogens, unlike their mouse counterparts. Subsequent quantitative RT-PCR confirmed up-regulation of expression of chondrogenic regulatory genes in lizard cells, including bmp2, sox9, runx2, and cartilage specific structural genes, aggrecan and collagen 2a1. Taken together, these data suggest that tail regeneration in lizards involves significant alterations in gene regulation with expanded musculoskeletal potency.

XGSA: A statistical method for cross-species gene set analysis.

MOTIVATION: Gene set analysis is a powerful tool for determining whether an experimentally derived set of genes is statistically significantly enriched for genes in other pre-defined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gene sets. Current gene set analysis methods do not facilitate comparing gene sets across different organisms as they do not explicitly deal with homology mapping between species. There lacks a systematic investigation about the effect of complex gene homology on cross-species gene set analysis. RESULTS: In this study, we show that not accounting for the complex homology structure when comparing gene sets in two species can lead to false positive discoveries, especially when comparing gene sets that have complex gene homology relationships. To overcome this bias, we propose a straightforward statistical approach, called XGSA, that explicitly takes the cross-species homology mapping into consideration when doing gene set analysis. Simulation experiments confirm that XGSA can avoid false positive discoveries, while maintaining good statistical power compared to other ad hoc approaches for cross-species gene set analysis. We further demonstrate the effectiveness of XGSA with two real-life case studies that aim to discover conserved or species-specific molecular pathways involved in social challenge and vertebrate appendage regeneration. AVAILABILITY AND IMPLEMENTATION: The R source code for XGSA is available under a GNU General Public License at http://github.com/VCCRI/XGSA CONTACT: jho@victorchang.edu.au.

hiHMM: Bayesian non-parametric joint inference of chromatin state maps.

MOTIVATION: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications. RESULTS: Here, we present a new Bayesian non-parametric method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in multiple genomes (different species, cell types and developmental stages) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them. We demonstrate the utility of this method using synthetic data as well as multiple modENCODE ChIP-seq datasets. CONCLUSION: The hierarchical and Bayesian non-parametric formulation in our approach is an important extension to the current set of methodologies for comparative chromatin landscape analysis. AVAILABILITY AND IMPLEMENTATION: Source codes are available at https://github.com/kasohn/hiHMM. Chromatin data are available at http://encode-x.med.harvard.edu/data_sets/chromatin/.

Spatial Transcriptional Mapping Reveals Site-Specific Pathways Underlying Human Atherosclerotic Plaque Rupture.

BACKGROUND: Atherosclerotic plaque ruptures, triggered by blood flow-associated biomechanical forces, cause most myocardial infarctions and strokes. OBJECTIVES: This study aims to investigate the exact location and underlying mechanisms of atherosclerotic plaque ruptures, identifying therapeutic targets against cardiovascular events. METHODS: Histology, electron microscopy, bulk and spatial RNA sequencing on human carotid plaques were studied in proximal, most stenotic, and distal regions along the longitudinal blood flow direction. Genome-wide association studies were used to examine heritability enrichment and causal relationships of atherosclerosis and stroke. Associations between top differentially expressed genes (DEGs) and preoperative and postoperative cardiovascular events were examined in a validation cohort. RESULTS: In human carotid atherosclerotic plaques, ruptures predominantly occurred in the proximal and most stenotic regions but not in the distal region. Histologic and electron microscopic examination showed that proximal and most stenotic regions exhibited features of plaque vulnerability and thrombosis. RNA sequencing identified DEGs distinguishing the proximal and most stenotic regions from the distal region which were deemed as most relevant to atherosclerosis-associated diseases as shown by heritability enrichment analyses. The identified pathways associated with the proximal rupture-prone regions were validated by spatial transcriptomics, firstly in human atherosclerosis. Of the 3 top DEGs, matrix metallopeptidase 9 emerged particularly because Mendelian randomization suggested that its high circulating levels were causally associated with atherosclerosis risk. CONCLUSIONS: Our findings show plaque site-specific transcriptional signatures associated with proximal rupture-prone regions of carotid atherosclerotic plaques. This led to the geographical mapping of novel therapeutic targets, such as matrix metallopeptidase 9, against plaque rupture.

Therapeutic potential of the Proprotein Convertase Subtilisin/Kexin family in vascular disease.

Proprotein convertase subtilisin/kexins (PCSKs) constitute a family of nine related proteases: PCSK1-7, MBTPS1, and PCSK9. Apart from PCSK9, little is known about PCSKs in cardiovascular disease. Here, we aimed to investigate the expression landscape and druggability potential of the entire PCSK family for CVD. We applied an integrative approach, combining genetic, transcriptomic and proteomic data from three vascular biobanks comprising carotid atherosclerosis, thoracic and abdominal aneurysms, with patient clinical parameters and immunohistochemistry of vascular biopsies. Apart from PCSK4, all PCSK family members lie in genetic regions containing variants associated with human cardiovascular traits. Transcriptomic analyses revealed that FURIN, PCSK5, MBTPS1 were downregulated, while PCSK6/7 were upregulated in plaques vs. control arteries. In abdominal aneurysms, FURIN, PCSK5, PCSK7, MBTPS1 were downregulated, while PCSK6 was enriched in diseased media. In thoracic aneurysms, only FURIN was significantly upregulated. Network analyses of the upstream and downstream pathways related to PCSKs were performed on the omics data from vascular biopsies, revealing mechanistic relationships between this protein family and disease. Cell type correlation analyses and immunohistochemistry showed that PCSK transcripts and protein levels parallel each other, except for PCSK9 where transcript was not detected, while protein was abundant in vascular biopsies. Correlations to clinical parameters revealed a positive association between FURIN plaque levels and serum LDL, while PCSK6 was negatively associated with Hb. PCSK5/6/7 were all positively associated with adverse cardiovascular events. Our results show that PCSK6 is abundant in plaques and abdominal aneurysms, while FURIN upregulation is characteristic for thoracic aneurysms. PCSK9 protein, but not the transcript, was present in vascular lesions, suggesting its accumulation from circulation. Integrating our results lead to the development of a novel 'molecular' 5D framework. Here, we conducted the first integrative study of the proprotein convertase family in this context. Our results using this translational pipeline, revealed primarily PCSK6, followed by PCSK5, PCSK7 and FURIN, as proprotein convertases with the highest novel therapeutic potential.

GDF15: emerging biology and therapeutic applications for obesity and cardiometabolic disease.

Growth differentiation factor 15 (GDF15) is a member of the TGFß superfamily whose expression is increased in response to cellular stress and disease as well as by metformin. Elevations in GDF15 reduce food intake and body mass in animal models through binding to glial cell-derived neurotrophic factor family receptor alpha-like (GFRAL) and the recruitment of the receptor tyrosine kinase RET in the hindbrain. This effect is largely independent of other appetite-regulating hormones (for example, leptin, ghrelin or glucagon-like peptide 1). Consistent with an important role for the GDF15-GFRAL signalling axis, some human genetic studies support an interrelationship with human obesity. Furthermore, findings in both mice and humans have shown that metformin and exercise increase circulating levels of GDF15. GDF15 might also exert anti-inflammatory effects through mechanisms that are not fully understood. These unique and distinct mechanisms for suppressing food intake and inflammation makes GDF15 an appealing candidate to treat many metabolic diseases, including obesity, type 2 diabetes mellitus, non-alcoholic fatty liver disease, cardiovascular disease and cancer cachexia. Here, we review the mechanisms regulating GDF15 production and secretion, GDF15 signalling in different cell types, and how GDF15-targeted pharmaceutical approaches might be effective in the treatment of metabolic diseases.

A neutralizing antibody against DKK1 does not reduce plaque formation in classical murine models of atherosclerosis: Is the therapeutic potential lost in translation?

BACKGROUND AND AIMS: Clinical interventions targeting nonlipid risk factors are needed given the high residual risk of atherothrombotic events despite effective control of dyslipidemia. Dickkopf-1 (DKK1) plays a lipid-independent role in vascular pathophysiology but its involvement in atherosclerosis development and its therapeutic attractiveness remain to be established. METHODS: Patient data, in vitro studies and pharmacological intervention in murine models of atherosclerosis were utilized. RESULTS: In patients' material (n = 127 late stage plaque specimens and n = 10 control vessels), DKK1 mRNA was found to be higher in atherosclerotic plaques versus control arteries. DKK1 protein was detected in the luminal intimal area and in the necrotic core of plaques. DKK1 was released from isolated primary human platelets (~12 - 21-fold) and endothelial cells (~1.4-2.5-fold) upon stimulation with different pathophysiological stimuli. In ApoE-/- and Ldlr-/- mice, plasma DKK1 concentrations were similar to those observed in humans, whereas DKK1 expression in different atheroprone arterial segments was very low/absent. Chronic treatment with a neutralizing DKK1 antibody effectively reduced plasma concentrations, however, plaque lesion area was not reduced in ApoE-/- and Ldlr-/- mice fed a western diet for 14 and 16 weeks. Anti-DKK1 treatment increased bone volume and bone mineral content. CONCLUSIONS: Functional inhibition of DKK1 with an antibody does not alter atherosclerosis progression in classical murine models. This may reflect the absence of DKK1 expression in plaques and more advanced animal disease models could be needed to evaluate the role and therapeutic attractiveness of DKK1 in late stage complications such as plaque destabilization, calcification, rupture and thrombosis.

Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus.

There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.

C3: An R package for cross-species compendium-based cell-type identification.

Cell type identification from an unknown sample can often be done by comparing its gene expression profile against a gene expression database containing profiles of a large number of cell-types. This type of compendium-based cell-type identification strategy is particularly successful for human and mouse samples because a large volume of data exists for these organisms. However, such rich data repositories often do not exist for most non-model organisms. This makes transcriptome-based sample classification in these species challenging. We propose to overcome this challenge by performing a cross-species compendium comparison. The key is to utilise a recently published cross-species gene set analysis (XGSA) framework to correct for biases that may arise due to potentially complex homologous gene mapping between two species. The framework is implemented as an open source R package called C3. We have evaluated the performance of C3 using a variety of public data in NCBI Gene Expression Omnibus. We also compared the functionality and performance of C3 against some similar gene expression profile matching tools. Our evaluation shows that C3 is a simple and effective method for cell type identification. C3 is available at https://github.com/VCCRI/C3.

Decoding the complex genetic causes of heart diseases using systems biology.

The pace of disease gene discovery is still much slower than expected, even with the use of cost-effective DNA sequencing and genotyping technologies. It is increasingly clear that many inherited heart diseases have a more complex polygenic aetiology than previously thought. Understanding the role of gene-gene interactions, epigenetics, and non-coding regulatory regions is becoming increasingly critical in predicting the functional consequences of genetic mutations identified by genome-wide association studies and whole-genome or exome sequencing. A systems biology approach is now being widely employed to systematically discover genes that are involved in heart diseases in humans or relevant animal models through bioinformatics. The overarching premise is that the integration of high-quality causal gene regulatory networks (GRNs), genomics, epigenomics, transcriptomics and other genome-wide data will greatly accelerate the discovery of the complex genetic causes of congenital and complex heart diseases. This review summarises state-of-the-art genomic and bioinformatics techniques that are used in accelerating the pace of disease gene discovery in heart diseases. Accompanying this review, we provide an interactive web-resource for systems biology analysis of mammalian heart development and diseases, CardiacCode ( http://CardiacCode.victorchang.edu.au/ ). CardiacCode features a dataset of over 700 pieces of manually curated genetic or molecular perturbation data, which enables the inference of a cardiac-specific GRN of 280 regulatory relationships between 33 regulator genes and 129 target genes. We believe this growing resource will fill an urgent unmet need to fully realise the true potential of predictive and personalised genomic medicine in tackling human heart disease.

How difficult is inference of mammalian causal gene regulatory networks?

Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.