BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource.

Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources. This enables gene expression dissection of brain networks with, to the best of our knowledge, an unprecedented coverage and allows for the identification of potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script-based toolboxes, and supports neuroscientists by directly leveraging the data instead of preparing it.

Stress deficits in reward behaviour are associated with and replicated by dysregulated amygdala-nucleus accumbens pathway function in mice.

Reduced reward interest/learning and reward-to-effort valuation are distinct, common symptoms in neuropsychiatric disorders for which chronic stress is a major aetiological factor. Glutamate neurons in basal amygdala (BA) project to various regions including nucleus accumbens (NAc). The BA-NAc neural pathway is activated by reward and aversion, with many neurons being monovalent. In adult male mice, chronic social stress (CSS) leads to reduced discriminative reward learning (DRL) associated with decreased BA-NAc activity, and to reduced reward-to-effort valuation (REV) associated, in contrast, with increased BA-NAc activity. Chronic tetanus toxin BA-NAc inhibition replicates the CSS-DRL effect and causes a mild REV reduction, whilst chronic DREADDs BA-NAc activation replicates the CSS effect on REV without affecting DRL. This study provides evidence that stress disruption of reward processing involves the BA-NAc neural pathway; the bi-directional effects implicate opposite activity changes in reward (learning) neurons and aversion (effort) neurons in the BA-NAc pathway following chronic stress.

Transcriptomic and proteomic profiling of young and old mice in the bleomycin model reveals high similarity.

The most common preclinical, in vivo model to study lung fibrosis is the bleomycin-induced lung fibrosis model in 2- to 3-mo-old mice. Although this model resembles key aspects of idiopathic pulmonary fibrosis (IPF), there are limitations in its predictability for the human disease. One of the main differences is the juvenile age of animals that are commonly used in experiments, resembling humans of around 20 yr. Because IPF patients are usually older than 60 yr, aging appears to play an important role in the pathogenesis of lung fibrosis. Therefore, we compared young (3 months) and old mice (21 months) 21 days after intratracheal bleomycin instillation. Analyzing lung transcriptomics (mRNAs and miRNAs) and proteomics, we found most pathways to be similarly regulated in young and old mice. However, old mice show imbalanced protein homeostasis as well as an increased inflammatory state in the fibrotic phase compared to young mice. Comparisons with published human transcriptomic data sets (GSE47460, GSE32537, and GSE24206) revealed that the gene signature of old animals correlates significantly better with IPF patients, and it also turned human healthy individuals better into "IPF patients" using an approach based on predictive disease modeling. Both young and old animals show similar molecular hallmarks of IPF in the bleomycin-induced lung fibrosis model, although old mice more closely resemble several features associated with IPF in comparison to young animals.

Transcriptome analysis of AAV-induced retinopathy models expressing human VEGF, TNF-α, and IL-6 in murine eyes.

Retinopathies are multifactorial diseases with complex pathologies that eventually lead to vision loss. Animal models facilitate the understanding of the pathophysiology and identification of novel treatment options. However, each animal model reflects only specific disease aspects and understanding of the specific molecular changes in most disease models is limited. Here, we conducted transcriptome analysis of murine ocular tissue transduced with recombinant Adeno-associated viruses (AAVs) expressing either human VEGF-A, TNF-α, or IL-6. VEGF expression led to a distinct regulation of extracellular matrix (ECM)-associated genes. In contrast, both TNF-α and IL-6 led to more comparable gene expression changes in interleukin signaling, and the complement cascade, with TNF-α-induced changes being more pronounced. Furthermore, integration of single cell RNA-Sequencing data suggested an increase of endothelial cell-specific marker genes by VEGF, while TNF-α expression increased the expression T-cell markers. Both TNF-α and IL-6 expression led to an increase in macrophage markers. Finally, transcriptomic changes in AAV-VEGF treated mice largely overlapped with gene expression changes observed in the oxygen-induced retinopathy model, especially regarding ECM components and endothelial cell-specific gene expression. Altogether, our study represents a valuable investigation of gene expression changes induced by VEGF, TNF-α, and IL-6 and will aid researchers in selecting appropriate animal models for retinopathies based on their agreement with the human pathophysiology.

Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.

Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG's tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.

Mapping the metabolomic and lipidomic changes in the bleomycin model of pulmonary fibrosis in young and aged mice.

Alterations in metabolic pathways were recently recognized as potential underlying drivers of idiopathic pulmonary fibrosis (IPF), translating into novel therapeutic targets. However, knowledge of metabolic and lipid regulation in fibrotic lungs is limited. To comprehensively characterize metabolic perturbations in the bleomycin mouse model of IPF, we analyzed the metabolome and lipidome by mass spectrometry. We identified increased tissue turnover and repair, evident by enhanced breakdown of proteins, nucleic acids and lipids and extracellular matrix turnover. Energy production was upregulated, including glycolysis, the tricarboxylic acid cycle, glutaminolysis, lactate production and fatty acid oxidation. Higher eicosanoid synthesis indicated inflammatory processes. Because the risk of IPF increases with age, we investigated how age influences metabolomic and lipidomic changes in the bleomycin-induced pulmonary fibrosis model. Surprisingly, except for cytidine, we did not detect any significantly differential metabolites or lipids between old and young bleomycin-treated lungs. Together, we identified metabolomic and lipidomic changes in fibrosis that reflect higher energy demand, proliferation, tissue remodeling, collagen deposition and inflammation, which might serve to improve diagnostic and therapeutic options for fibrotic lung diseases in the future.

Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. In adults with NAFLD, fibrosis can develop and progress to liver cirrhosis and liver failure. However, the underlying molecular mechanisms of fibrosis progression are not fully understood. Using total RNA-Seq, we investigated the molecular mechanisms of NAFLD and fibrosis. We sequenced liver tissue from 143 adults across the full spectrum of fibrosis stage including those with stage 4 fibrosis (cirrhosis). We identified gene expression clusters that strongly correlate with fibrosis stage including four genes that have been found consistently across previously published transcriptomic studies on NASH i.e. COL1A2, EFEMP2, FBLN5 and THBS2. Using cell type deconvolution, we estimated the loss of hepatocytes versus gain of hepatic stellate cells, macrophages and cholangiocytes with advancing fibrosis stage. Hepatocyte-specific functional analysis indicated increase of pro-apoptotic pathways and markers of bipotent hepatocyte/cholangiocyte precursors. Regression modelling was used to derive predictors of fibrosis stage. This study elucidated molecular and cell composition changes associated with increasing fibrosis stage in NAFLD and defined informative gene signatures for the disease.

HieRFIT: a hierarchical cell type classification tool for projections from complex single-cell atlas datasets.

MOTIVATION: The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. RESULTS: Here, we present a new cell type projection tool, Hierarchical Random Forest for Information Transfer (HieRFIT), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real-life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. AVAILABILITY AND IMPLEMENTATION: HieRFIT is implemented as an R package, and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy.

Diabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP-PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and ß-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.

A dual druggable genome-wide siRNA and compound library screening approach identifies modulators of parkin recruitment to mitochondria.

Genetic and biochemical evidence points to an association between mitochondrial dysfunction and Parkinson's disease (PD). PD-associated mutations in several genes have been identified and include those encoding PTEN-induced putative kinase 1 (PINK1) and parkin. To identify genes, pathways, and pharmacological targets that modulate the clearance of damaged or old mitochondria (mitophagy), here we developed a high-content imaging-based assay of parkin recruitment to mitochondria and screened both a druggable genome-wide siRNA library and a small neuroactive compound library. We used a multiparameter principal component analysis and an unbiased parameter-agnostic machine-learning approach to analyze the siRNA-based screening data. The hits identified in this analysis included specific genes of the ubiquitin proteasome system, and inhibition of ubiquitin-conjugating enzyme 2 N (UBE2N) with a specific antagonist, Bay 11-7082, indicated that UBE2N modulates parkin recruitment and downstream events in the mitophagy pathway. Screening of the compound library identified kenpaullone, an inhibitor of cyclin-dependent kinases and glycogen synthase kinase 3, as a modulator of parkin recruitment. Validation studies revealed that kenpaullone augments the mitochondrial network and protects against the complex I inhibitor MPP+. Finally, we used a microfluidics platform to assess the timing of parkin recruitment to depolarized mitochondria and its modulation by kenpaullone in real time and with single-cell resolution. We demonstrate that the high-content imaging-based assay presented here is suitable for both genetic and pharmacological screening approaches, and we also provide evidence that pharmacological compounds modulate PINK1-dependent parkin recruitment.

DMSO cryopreservation is the method of choice to preserve cells for droplet-based single-cell RNA sequencing.

Combining single-cell RNA sequencing (scRNA-seq) with upstream cell preservation procedures such as cryopreservation or methanol fixation has recently become more common. By separating cell handling and preparation, from downstream library generation, scRNA-seq workflows are more flexible and manageable. However, the inherent transcriptomic changes associated with cell preservation and how they may bias further downstream analysis remain unknown. Here, we present a side-by-side droplet-based scRNA-seq analysis, comparing the gold standard - fresh cells - to three different cell preservation workflows: dimethyl sulfoxide based cryopreservation, methanol fixation and CellCover reagent. Cryopreservation proved to be the most robust protocol, maximizing both cell integrity and low background ambient RNA. Importantly, gene expression profiles from fresh cells correlated most with those of cryopreserved cells. Such similarities were consistently observed across the tested cell lines (R ≥ 0.97), monocyte-derived macrophages (R = 0.97) and immune cells (R = 0.99). In contrast, both methanol fixation and CellCover preservation showed an increased ambient RNA background and an overall lower gene expression correlation to fresh cells. Thus, our results demonstrate the superiority of cryopreservation over other cell preservation methods. We expect our comparative study to provide single-cell omics researchers invaluable support when integrating cell preservation into their scRNA-seq studies.

FELLA: an R package to enrich metabolomics data.

BACKGROUND: Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects. RESULTS: We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature. CONCLUSIONS: The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.

BATLAS: Deconvoluting Brown Adipose Tissue.

Recruitment and activation of thermogenic adipocytes have received increasing attention as a strategy to improve systemic metabolic control. The analysis of brown and brite adipocytes is complicated by the complexity of adipose tissue biopsies. Here, we provide an in-depth analysis of pure brown, brite, and white adipocyte transcriptomes. By combining mouse and human transcriptome data, we identify a gene signature that can classify brown and white adipocytes in mice and men. Using a machine-learning-based cell deconvolution approach, we develop an algorithm proficient in calculating the brown adipocyte content in complex human and mouse biopsies. Applying this algorithm, we can show in a human weight loss study that brown adipose tissue (BAT) content is associated with energy expenditure and the propensity to lose weight. This online available tool can be used for in-depth characterization of complex adipose tissue samples and may support the development of therapeutic strategies to increase energy expenditure in humans.

Null diffusion-based enrichment for metabolomics data.

Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of 13C glucose labelling of these cells.

Intensity drift removal in LC/MS metabolomics by common variance compensation.

UNLABELLED: Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context. AVAILABILITY AND IMPLEMENTATION: The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit).

UNLABELLED: Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results. MAIT generates output files with the statistical results, peak annotation and metabolite identification. AVAILABILITY AND IMPLEMENTATION: http://b2slab.upc.edu/software-and-downloads/metabolite-automatic-identification-toolkit/.

Peak aggregation as an innovative strategy for improving the predictive power of LC-MS metabolomic profiles.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomic datasets consist of different features including (de)protonated molecules, fragments, adducts, and isotopes that may show high correlation values related to a high level of collinearity. There have been described several sources of these high correlation patterns regarding metabolomic datasets. Among these sources, it should be highlighted the high level of correlation computed between features coming from the same metabolite. It is well-known that soft ionization methods (such as electrospray) produce several mass features from a particular compound (i.e., metabolite spectrum). Typically, the statistical methods used in metabolomics consider spectral peaks as variables. However, it has been reported that a high collinearity between variables might be the responsible for high uncertainty values in the predictors of a regression. In this context, this technical note proposes a new strategy based on the application of the so-called peak aggregation methods (NMF Reduction, PCA Decomposition, Maximum Peak, and Spectrum Mean) to take advantage of the variable collinearity and solve the issue of high variable collinearity. A set of real samples obtained after human nutritional intervention with placebo or polyphenol-rich beverages was used to test this methodology. The results showed that applying any peak aggregation method (especially NMF and PCA) improves the statistical prediction power of class pertinence independently of the nature of the classifier (linear PLS-DA or nonlinear SVM). Overall, the introduction of this new approach resulted in a reduction of the dimensionality of the data and, in addition, in a significant increase in the overall predictive power of the data.