Genes positively regulated by Mef2c in cortical neurons are enriched for common genetic variation associated with IQ and educational attainment.

The myocyte enhancer factor 2 C (MEF2C) gene encodes a transcription factor important for neurogenesis and synapse development and contains common variants associated with intelligence (IQ) and educational attainment (EA). Here, we took gene expression data from the mouse cortex of a Mef2c mouse model with a heterozygous DNA binding-deficient mutation of Mef2c (Mef2c-het) and combined these data with MEF2C ChIP-seq data from cortical neurons and single-cell data from the mouse brain. This enabled us to create a set of genes that were differentially regulated in Mef2c-het mice, represented direct target genes of MEF2C and had elevated in expression in cortical neurons. We found this gene-set to be enriched for genes containing common genetic variation associated with IQ and EA. Genes within this gene-set that were down-regulated, i.e. have reduced expression in Mef2c-het mice versus controls, were specifically significantly enriched for both EA and IQ associated genes. These down-regulated genes were enriched for functionality in the adenylyl cyclase signalling system, which is known to positively regulate synaptic transmission and has been linked to learning and memory. Within the adenylyl cyclase signalling system, three genes regulated by MEF2C, CRHR1, RGS6, and GABRG3, are associated at genome-wide significant levels with IQ and/or EA. Our results indicate that genetic variation in MEF2C and its direct target genes within cortical neurons contribute to variance in cognition within the general population, and the molecular mechanisms involved include the adenylyl cyclase signalling system's role in synaptic function.

Computational approaches and challenges in the analysis of circRNA data.

Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.

nf-core/circrna: a portable workflow for the quantification, miRNA target prediction and differential expression analysis of circular RNAs.

BACKGROUND: Circular RNAs (circRNAs) are a class of covalenty closed non-coding RNAs that have garnered increased attention from the research community due to their stability, tissue-specific expression and role as transcriptional modulators via sequestration of miRNAs. Currently, multiple quantification tools capable of detecting circRNAs exist, yet none delineate circRNA-miRNA interactions, and only one employs differential expression analysis. Efforts have been made to bridge this gap by way of circRNA workflows, however these workflows are limited by both the types of analyses available and computational skills required to run them. RESULTS: We present nf-core/circrna, a multi-functional, automated high-throughput pipeline implemented in nextflow that allows users to characterise the role of circRNAs in RNA Sequencing datasets via three analysis modules: (1) circRNA quantification, robust filtering and annotation (2) miRNA target prediction of the mature spliced sequence and (3) differential expression analysis. nf-core/circrna has been developed within the nf-core framework, ensuring robust portability across computing environments via containerisation, parallel deployment on cluster/cloud-based infrastructures, comprehensive documentation and maintenance support. CONCLUSION: nf-core/circrna reduces the barrier to entry for researchers by providing an easy-to-use, platform-independent and scalable workflow for circRNA analyses. Source code, documentation and installation instructions are freely available at https://nf-co.re/circrna and https://github.com/nf-core/circrna .

Genes influenced by MEF2C contribute to neurodevelopmental disease via gene expression changes that affect multiple types of cortical excitatory neurons.

Myocyte enhancer factor 2 C (MEF2C) is an important transcription factor during neurodevelopment. Mutation or deletion of MEF2C causes intellectual disability (ID), and common variants within MEF2C are associated with cognitive function and schizophrenia risk. We investigated if genes influenced by MEF2C during neurodevelopment are enriched for genes associated with neurodevelopmental phenotypes and if this can be leveraged to identify biological mechanisms and individual brain cell types affected. We used a set of 1055 genes that were differentially expressed in the adult mouse brain following early embryonic deletion of Mef2c in excitatory cortical neurons. Using genome-wide association studies data, we found these differentially expressed genes (DEGs) to be enriched for genes associated with schizophrenia, intelligence and educational attainment but not autism spectrum disorder (ASD). For this gene set, genes that overlap with target genes of the Fragile X mental retardation protein (FMRP) are a major driver of these enrichments. Using trios data, we found these DEGs to be enriched for genes containing de novo mutations reported in ASD and ID, but not schizophrenia. Using single-cell RNA sequencing data, we identified that a number of different excitatory glutamatergic neurons in the cortex were enriched for these DEGs including deep layer pyramidal cells and cells in the retrosplenial cortex, entorhinal cortex and subiculum, and these cell types are also enriched for FMRP target genes. The involvement of MEF2C and FMRP in synapse elimination suggests that disruption of this process in these cell types during neurodevelopment contributes to cognitive function and risk of neurodevelopmental disorders.

Molecular signatures and clinical outcomes of transplant glomerulopathy stratified by microvascular inflammation and donor-specific antibody.

BACKGROUND: We investigated clinical outcomes and molecular signatures of transplant glomerulopathy (TG) stratified by microvascular inflammation (MVI) and donor-specific antibody (DSA) status. METHODS: We performed a retrospective review of 749 kidney transplant patients who received a for-cause kidney biopsy from 2009 to 2014. We classified TG as MVI positive (MVI+) or MVI negative (MVI-), and with or without DSA. We obtained gene expression profiles for 44 biopsies by Affymetrix HuGene 1.0 ST expression arrays. RESULTS: A total of 100 patients had TG; 49 were MVI+, and 51 were MVI-. After a median post-biopsy follow-up of 2.08 years (range 0.43-4.59), Kaplan-Meier survival analysis demonstrated worse allograft survival in MVI+ TG patients compared with MVI- TG patients (P = 0.01), and time to graft failure was significantly shorter in MVI+ patients (1.08 ± 1.01 years vs 2.3 ± 1.8 years; P = 0.002). DSA status did not affect graft survival within MVI+ or MVI- groups. Analysis of pathogenesis-based transcripts (PBT) showed that MVI+ TG biopsies had increased expression of gamma interferon and rejection (GRIT) and DSA-associated transcripts (DSAST), as observed in antibody-mediated rejection. MVI- TG biopsies had increased expression of cytotoxic and regulatory T cell- and B cell-associated transcripts but not GRIT or DSAST. DSA status had no effect on expression of any PBTs studied in MVI- TG biopsies. CONCLUSIONS: Graft survival in TG is significantly worse in the presence of MVI. Gene expression profiles of MVI+ TG resemble antibody-mediated rejection while gene expression profiles of MVI- TG resemble cell-mediated rejection regardless of DSA status.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.

BACKGROUND: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). METHODS: Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. RESULTS: The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. CONCLUSIONS: Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.

CCPlotR: an R package for the visualization of cell-cell interactions.

Summary: We present CCPlotR-an R package that generates visualizations of cell-cell interactions. CCPlotR is designed to work with the output of tools that predict cell-cell interactions from single-cell gene expression data and requires only a table of predicted interactions as input. The package can generate a comprehensive set of publication-ready figures such as heatmaps, dotplots, circos plots and network diagrams, providing a useful resource for researchers working on cell-cell interactions. Availability and implementation: CCPlotR is available to download and install from GitHub (https://github.com/Sarah145/CCPlotR) and comes with a toy dataset to demonstrate the different functions. Support for users will be provided via the GitHub issues tracker (https://github.com/Sarah145/CCPlotR/issues).

Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia.

The bone marrow (BM) is a complex microenvironment, coordinating the production of billions of blood cells every day. Despite its essential role and its relevance to hematopoietic diseases, this environment remains poorly characterized. Here we present a high-resolution characterization of the niche in health and acute myeloid leukemia (AML) by establishing a single-cell gene expression database of 339,381 BM cells. We found significant changes in cell type proportions and gene expression in AML, indicating that the entire niche is disrupted. We then predicted interactions between hematopoietic stem and progenitor cells (HSPCs) and other BM cell types, revealing a remarkable expansion of predicted interactions in AML that promote HSPC-cell adhesion, immunosuppression, and cytokine signaling. In particular, predicted interactions involving transforming growth factor ß1 (TGFB1) become widespread, and we show that this can drive AML cell quiescence in vitro. Our results highlight potential mechanisms of enhanced AML-HSPC competitiveness and a skewed microenvironment, fostering AML growth.

An integrated genome screen identifies the Wnt signaling pathway as a major target of WT1.

WT1, a critical regulator of kidney development, is a tumor suppressor for nephroblastoma but in some contexts functions as an oncogene. A limited number of direct transcriptional targets of WT1 have been identified to explain its complex roles in tumorigenesis and organogenesis. In this study we performed genome-wide screening for direct WT1 targets, using a combination of ChIP-ChIP and expression arrays. Promoter regions bound by WT1 were highly G-rich and resembled the sites for a number of other widely expressed transcription factors such as SP1, MAZ, and ZNF219. Genes directly regulated by WT1 were implicated in MAPK signaling, axon guidance, and Wnt pathways. Among directly bound and regulated genes by WT1, nine were identified in the Wnt signaling pathway, suggesting that WT1 modulates a subset of Wnt components and responsive genes by direct binding. To prove the biological importance of the interplay between WT1 and Wnt signaling, we showed that WT1 blocked the ability of Wnt8 to induce a secondary body axis during Xenopus embryonic development. WT1 inhibited TCF-mediated transcription activated by Wnt ligand, wild type and mutant, stabilized beta-catenin by preventing TCF4 loading onto a promoter. This was neither due to direct binding of WT1 to the TCF binding site nor to interaction between WT1 and TCF4, but by competition of WT1 and TCF4 for CBP. WT1 interference with Wnt signaling represents an important mode of its action relevant to the suppression of tumor growth and guidance of development.

The Einstein Genome Gateway using WASP - a high throughput multi-layered life sciences portal for XSEDE.

Massively-parallel sequencing (MPS) technologies and their diverse applications in genomics and epigenomics research have yielded enormous new insights into the physiology and pathophysiology of the human genome. The biggest hurdle remains the magnitude and diversity of the datasets generated, compromising our ability to manage, organize, process and ultimately analyse data. The Wiki-based Automated Sequence Processor (WASP), developed at the Albert Einstein College of Medicine (hereafter Einstein), uniquely manages to tightly couple the sequencing platform, the sequencing assay, sample metadata and the automated workflows deployed on a heterogeneous high performance computing cluster infrastructure that yield sequenced, quality-controlled and 'mapped' sequence data, all within the one operating environment accessible by a web-based GUI interface. WASP at Einstein processes 4-6 TB of data per week and since its production cycle commenced it has processed ~ 1 PB of data overall and has revolutionized user interactivity with these new genomic technologies, who remain blissfully unaware of the data storage, management and most importantly processing services they request. The abstraction of such computational complexity for the user in effect makes WASP an ideal middleware solution, and an appropriate basis for the development of a grid-enabled resource - the Einstein Genome Gateway - as part of the Extreme Science and Engineering Discovery Environment (XSEDE) program. In this paper we discuss the existing WASP system, its proposed middleware role, and its planned interaction with XSEDE to form the Einstein Genome Gateway.