Biology subjects

Ramos, M.

Publications and source records attributed to Ramos, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Alcama expressed in blood retina barrier and Muller glia is involved in zebrafish retina regeneration

Activated leukocyte cell adhesion molecule A (Alcama) plays a role in axonal guidance, cell differentiation, and retinal lamination in a developing retina and was identified as a marker for activated Muller glial cells in adult zebrafish. However, its spatiotemporal localization and its involvement in retina regeneration remains unclear. Here we induced focal photoreceptor damage in zebrafish using laser photocoagulation and examined the expression and localization of Alcama at different time points post lesion. Immunohistochemistry in wild type fish and Tg(kdrl-EGFP) fish showed Alcama localized to the blood retina barrier with increased expression in Muller glial end feet and radial processes in a regenerating retina. To confirm its role in retina regeneration, alcama expression was transiently knocked down using morpholinos in adult fish. Scanning laser ophthalmoscopy, Zpr1 immunostaining and EdU staining showed delayed retina regeneration in alcama knockdown fish, indicating a possible role for Alcama in zebrafish retina regeneration.

cell biology↗

bioRxiv2026

CSLB4-mediated cell wall remodeling decouples phloem access from aphid performance in Arabidopsis thaliana

The plant cell wall (CW) is a key determinant of plant defense; however, the extent to which natural variation in CW architecture contributes to resistance against phloem-feeding insects remains unclear. Here, we combined genome-wide association studies (GWAS) with functional analyses to identify genetic determinants of resistance against the specialist aphid Brevicoryne brassicae in Arabidopsis thaliana. GWAS conducted on 200 natural accessions identified a single locus on chromosome 2 associated with aphid performance. Integration of haplotype and epidermis-specific expression data prioritized CSLB4, a member of the cellulose synthase-like B family. Loss-of-function cslb4 mutants showed reduced aphid offspring, indicating enhanced resistance to B. brassicae, whereas performance of the generalist aphid Myzus persicae was unaffected. Electrical penetration graph analyses revealed earlier phloem access on cslb4 mutants despite reduced performance, indicating a decoupling between phloem access and aphid success. Biochemical and immunolocalization analyses showed that CSLB4 disruption altered CW architecture, including increased xyloglucan epitope accessibility in mesophyll cell walls and reduced callose deposition upon aphid infestation. In addition, CSLB4 localized to Golgi-associated compartments, and in silico analyses are consistent with a role in non-cellulosic polysaccharide biosynthesis. Together, these findings identified CSLB4 as a modulator of CW architecture that uncouples phloem access from aphid performance. HighlightA GWAS identifies CSLB4 as a regulator of cell wall architecture that uncouples aphid-feeding from performance, revealing a new mechanism of plant resistance to specialist insects.

plant biology↗

bioRxiv2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

bioRxiv2025

Orchestrating Microbiome Analysis with Bioconductor

The expansion of microbiome research has led to the accumulation of interlinked datasets encompassing versatile taxonomic and functional assays. The analysis of increasingly large and heterogeneous multi-modal microbiome data would benefit from unified approaches supporting the design of modular data science workflows through interoperable methods. The Bioconductor project has recently developed an optimized statistical programming framework for multi-assay data integration. Building on this foundation, we introduce a community-developed open source ecosystem for microbiome data science. In contrast to the previous alternatives, the methodology is specifically designed to support joint analysis of hierarchical, interlinked, and heterogeneous multi-table datasets that are increasingly common in modern microbiome research. This data science ecosystem encompasses open data, methods, tutorials, and an active online community. These resources support standardized and reproducible data wrangling, joint analysis, and reporting. We have detailed the functionality and usage in the online book https://microbiome.github.io/OMA, which offers guidance for prospective users and contributors.

bioinformatics↗

bioRxiv2025

Mutations in specific MT5-MMP domains prevent the accumulation of toxic APP metabolites and serve as templates for peptide-based therapeutics in cell models of Alzheimer's disease

Our prior work established a pivotal role for membrane-type 5-matrix metalloproteinase (MT5-MMP) in Alzheimers disease (AD) pathogenesis, particularly through its C-terminal transmembrane (TM) and intracellular (IC) domains, which influence the fate of major toxic metabolites of toxic amyloid precursor protein (APP) metabolites, particularly C99 and A{beta}. Hypothesizing that modifications in these domains could modulate C99 and A{beta} levels, we engineered MT5-MMP variants with deletions or substitutions in the TM/IC domains or specific IC amino acid clusters. When co-transfected into human cell lines accumulating C99, certain IC domain mutations promoted C99 degradation and reduced A{beta} levels, while other mutations had divergent effects. High content imaging further revealed that MT5-MMP IC domain modification altered C99 subcellular trafficking within the endomembrane system, impacting its processing. Proximity ligation assays confirmed the importance of the IC domain in MT5-MMP colocalization and potential interaction with C99. To translate these findings, we synthetized peptides mimicking the MT5-MMP IC domain, incorporating mutations that reduce C99 and/or A{beta} levels. One peptide effectively lowered C99 levels in an in vitro AD model. Overall, this study highlights the importance of specific amino acids in the C-terminal domains of MT5-MMP for regulating C99 and A{beta} metabolism. It also provides new insights for developing MT5-MMP-based therapeutic strategies against AD, exploiting the unique properties of specific mutations in MT5-MMP to prevent toxic accumulation of C99 and A{beta}. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/655451v3_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@a6b04aorg.highwire.dtl.DTLVardef@1800721org.highwire.dtl.DTLVardef@542505org.highwire.dtl.DTLVardef@4a6d7b_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

cell biology↗

bioRxiv2025

AAV-mediated ARSA replacement for the treatment of Metachromatic Leukodystrophy

Metachromatic leukodystrophy (MLD) is an autosomal recessive neurodegenerative disorder caused by mutations in the arylsulfatase A (ARSA) gene, resulting in lower sulfatase activity and the toxic accumulation of sulfatides in the central and peripheral nervous system. Children account for 70% of cases and become progressively disabled with death occurring within 10 years of disease onset. Gene therapy approaches to restore ARSA expression via adeno-associated viral vectors (AAV) have been promising but hampered by limited brain biodistribution. We report the development of a novel capsid AAV.GMU01, demonstrating superior biodistribution and transgene expression in the central nervous system of non-human primates (NHPs). Next, we show that AAV.GMU01-ARSA treated MLD mice exhibit persistent, normal levels of sulfatase activity and a concomitant reduction in toxic sulfatides. Treated mice also show a reduction in MLD-associated pathology and auditory dysfunction. Lastly, we demonstrate that treatment with AAV.GMU01-ARSA in NHPs is well-tolerated and results in potentially therapeutic ARSA expression in the brain. In summary, we propose AAV.GMU01-ARSA mediated gene replacement as a clinically viable approach to achieve broad and therapeutic levels of ARSA.

neuroscience↗

bioRxiv2025

Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth

Previous benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and "real data" examples, but to the best of our knowledge, none have yet employed experimental data with known "ground truth" differential abundance. A key debate in the field centers on whether compositional methods are necessary for DA analysis, which is challenging to answer due to the lack of ground truth data. To address this gap, we created the Bioconductor data package MicrobiomeBenchmarkData, featuring three microbiome datasets with established biological ground truths: 1) diverse oral microbiomes from supragingival and subgingival plaques, expected to favor aerobic and anaerobic bacteria, respectively, 2) low-diversity microbiomes from healthy vaginas and bacterial vaginosis, conditions that have been well-characterized through cell culture and microscopy, and 3) a spike-in dataset with constant, known absolute abundances of three bacteria. We benchmarked 17 DA approaches and demonstrated that compositional DA methods are not beneficial but rather lack sensitivity, show increased variability in constant-abundance spike-ins, and, most surprisingly, more frequently produce paradoxical results with DA in the wrong direction for the low-diversity microbiome. Conversely, commonly used methods in microbiome literature, such as LEfSe, the Wilcoxon test, and RNA-seq-derived methods, performed best. We conclude that researchers continue using widely adopted non-parametric or RNA-seq DA methods and that further development of compositional methods includes benchmarking against datasets with known biological ground truth.

bioinformatics↗

bioRxiv2024

Immunologic and Biophysical Features of the BNT162b2 JN.1- and KP.2-Adapted COVID-19 Vaccines

Vaccines remain a vital public health tool to reduce the burden of COVID-19. COVID-19 vaccines that are more closely matched to circulating SARS-CoV-2 lineages elicit more potent and relevant immune responses that translate to improved real-world vaccine effectiveness. The rise in prevalence of the Omicron JN.1 lineage, and subsequent derivative sublineages such as KP.2 and KP.3, coincided with reduced neutralizing activity and effectiveness of Omicron XBB.1.5-adapted vaccines. Here, we characterized the biophysical and immunologic attributes of BNT162b2 JN.1- and KP.2-adapted mRNA vaccine-encoded spike (S) protein immunogens. Biophysical interrogations of S revealed the structural consequences of hallmark amino acid substitutions and a potential molecular mechanism of immune escape employed by JN.1 and KP.2. The vaccine candidates were evaluated for their immunogenicity when administered as fourth or fifth doses in BNT162b2-experienced mice or as a primary series in naive mice. In both vaccine-experienced and naive settings, JN.1- and KP.2-adapted vaccines conferred improved neutralizing responses over the BNT162b2 XBB.1.5 vaccine against a broad panel of emerging JN.1 sublineages, including the predominant KP.3.1.1 and emerging XEC lineages. Antigenic mapping of neutralizing responses indicated greater antigenic overlap of JN.1- and KP.2-adapted vaccine responses with currently circulating sublineages compared to an XBB.1.5-adapted vaccine. CD4+ and CD8+ T cell responses were generally conserved across all three vaccines. Together, the data support the selection of JN.1- or KP.2-adapted vaccines for the 2024-25 COVID-19 vaccine formula. ONE-SENTENCE SUMMARYThe Omicron JN.1- and KP.2-adapted BNT162b2 mRNA vaccines encoding prefusion S proteins elicit similar preclinical neutralizing antibody responses against circulating JN.1 sublineage pseudoviruses that are more potent than those elicited by past iterations of BNT162b2 licensed vaccines, thus demonstrating the importance of annual strain changes to the COVID-19 vaccine.

immunology↗

bioRxiv2024

Osmotic Stress Influences Microtubule Drug Response Via WNK1 Kinase Signaling

Ion homeostasis is critical for numerous cellular processes, and disturbances in ionic balance underlie diverse pathological conditions, including cancer progression. Targeting ion homeostasis is even considered as a strategy to treat cancer. However, very little is known about how ion homeostasis may influence anticancer drug response. In a genome-wide CRISPR-Cas9 resistance drug screen, we identified and validated the master osmostress regulator WNK1 kinase as a modulator of the response to the mitotic drug rigosertib. Osmotic stress and WNK1 inactivation lead to an altered response not only to rigosertib treatment but also to other microtubule-related drugs, minimizing the prototypical mitotic arrest produced by these drugs. This effect is due to an alteration in microtubule stability and polymerization dynamics, likely maintained by fluctuations in intracellular molecular crowding upon WNK1 inactivation. This promotes resistance to microtubule depolymerizing drugs, and increased sensitivity to microtubule stabilizing drugs. In summary, our data proposes WNK1 osmoregulation activity as a biomarker for microtubule-associated chemotherapy response.

cancer biology↗

bioRxiv2023

bamSliceR: cross-cohort variant and allelic bias analysis for rare variants and rare diseases

The NCI Genomic Data Commons (GDC) provides controlled access to sequencing data from thousands of subjects, enabling large-scale study of impactful genetic alterations such as simple and complex germline and structural variants. However, efficient analysis requires significant computational resources and expertise, especially when recalling variants from raw sequence reads. We thus developed bamSliceR, an R/Bioconductor package that builds upon the GenomicDataCommons package to extract aligned sequence reads from cross-GDC meta-cohorts, followed by targeted analysis of variants and effects (including transcript-aware variant annotation from transcriptome-aligned GDC RNA data). Here we demonstrate population-scale genomic & transcriptomic analyses with minimal compute burden via bamSliceR, identifying recurrent, clinically relevant sequence and structural variants in the TARGET AML and BEAT-AML cohorts. We then validate results in the (non-GDC) Leucegene cohort, demonstrating how the bamSliceR pipeline can be seamlessly applied to replicate findings in non-GDC cohorts. These variants directly yield clinically impactful and biologically testable hypotheses for mechanistic investigation. bamSliceR has been submitted to the Bioconductor project, where it is presently under review, and is available on GitHub at https://github.com/trichelab/bamSliceR.

bioinformatics↗

bioRxiv2022

Chronic chromosome instability induced by Plk1 results in immune suppression in breast cancer

Chromosomal instability (CIN), the inability to correctly segregate chromosomes during cell division, is a common characteristic of solid tumors. CIN contributes to tumor evolution by promoting intratumor heterogeneity, thus facilitating resistance to cancer therapies. In vitro studies have demonstrated that cells with complex karyotypes are recognized and eliminated by natural killer (NK) cells. Paradoxically, it has also been observed that human tumors with high levels of CIN have an immunosuppressive phenotype. It remains unclear which CIN-associated molecular features alter immune recognition during tumor evolution. Previous studies with Polo-like kinase 1 (Plk1) overexpression in Her2-positive breast tumors, resulted in increased levels of CIN and delayed tumorigenesis. Using this mouse model, we show that high CIN tumors activate a senescence-associated secretory phenotype (SASP) and become immune evasive by activating RELB signaling and upregulating PD-L1 in a non-cell-autonomous manner. Single-cell RNA sequencing of immune cells from early-stage induced mammary glands revealed that macrophages, NK cells, B cells and regulatory T cells are programmed to a suppressive phenotype during tumor development. In human tumors, we further establish the importance of RELB/p38 signaling in understanding the interplay between CIN and the immune system, highlighting the need for novel adjuvant therapies in the context of chromosomally unstable tumors.

cancer biology↗

bioRxiv2021

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

neuroscience↗

bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

bioinformatics↗

bioRxiv2021

GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases

Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. We performed Principal Component Analysis on 536 studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors were aggregated to form Replicable Axes of Variation (RAV). RAVs were annotated with metadata of originating studies and samples and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrated the efficient and coherent database searching, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature will aid analyzing new gene expression data in the context of existing databases using minimal computing resources. PURPOSEMillions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. Existing methods for leveraging these public resources have focused on the reanalysis of existing data or analysis of new datasets independently. We present a novel approach to interpreting new transcriptomic datasets by near-instantaneous comparison to public archives without high-performance computing requirements. METHODSTo identify replicable and interpretable axes of variation in any given gene expression dataset, we performed Principal Component Analysis (PCA) on 536 studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors, when compared across studies, were aggregated to form Replicable Axes of Variation (RAV). RAVs were annotated with metadata of originating studies and samples and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. RESULTSRAVs are robust to batch effects and the presence of low-quality or irrelevant studies, and identify signals that can be lost by merging samples across the training datasets. The GenomicSuperSignature package allows instantaneous matching of PCA axes in new datasets to pre-computed RAVs, cutting down the analysis time from days to the order of seconds on an ordinary laptop. We demonstrate that RAVs associated with a phenotype can provide insight into weak or indirectly measured biological attributes in a new study by leveraging accumulated data from published datasets. Benchmarking against complementary previous works demonstrates that the RAV index 1) identifies colorectal carcinoma transcriptome subtypes that are similar to but more correlated with clinicopathological characteristics than previous disease-specific efforts and 2) can estimate neutrophil counts through transfer learning on new data comparably to the previous efforts despite major differences in training datasets and model building processes with the additional benefits of flexibility and scalability of the model application. CONCLUSIONGenomicSuperSignature establishes an information resource and software tools to interrogate it. Prior knowledge databases are coherently linked, enabling researchers to analyze new gene expression data in the context of existing databases using minimal computing resources. The robustness of GenomicSuperSignature suggests that we can expand this approach beyond human gene expression profiles, such as single-cell RNA-seq, microbiome abundance, and different species transcriptomics datasets.

bioinformatics↗

bioRxiv2021

Set4 coordinates the activity of histone deacetylases and regulates stress-responsive gene expression within subtelomeric regions in yeast

The yeast chromatin protein Set4 is a member of the Set3-subfamily of SET domain proteins which play critical roles in the regulation of gene expression in diverse developmental and environmental contexts, although they appear to lack methyltransferase activity. The molecular functions of Set4 are relatively unexplored, likely due to its low abundance in standard growth conditions. We previously reported that Set4 promotes survival during oxidative stress and regulates expression of stress response genes via stress-dependent chromatin localization. In this study, global gene expression analysis and investigation of histone modification status has revealed a role for Set4 in maintaining gene repressive mechanisms within yeast subtelomeres under both normal and stress conditions. We show that Set4 works in a partially overlapping pathway to the SIR complex and the histone deacetylase Rpd3 to maintain proper levels of histone acetylation and expression of stress response genes encoded in subtelomeres. This role for Set4 is particularly critical for cells under hypoxic conditions, and the loss of Set4 decreases cell fitness and cell wall integrity in hypoxia. These findings uncover a new regulator of subtelomeric chromatin that is key to stress defense pathways and demonstrate a function for yeast Set4 in regulating repressive, heterochromatin-like environments.

genetics↗

bioRxiv2021

Combined Ventilation Of Two Subjects With A Single Mechanical Ventilator Using A New Medical Device: An In Vitro Study

INTRODUCTIONThe SARS-CoV2 pandemic has created a sudden lack of ventilators. DuplicAR(R) is a novel device that allows simultaneous and independent ventilation of two subjects with a single ventilator. The aims of this study are: a) to determine the efficacy of DuplicAR(R) to independently regulate the peak and positive-end expiratory pressures in each subject, both under pressure-controlled ventilation and volume-controlled ventilation, and b) to determine the ventilation mode in which DuplicAR(R) presents the best performance and safety. MATERIALS AND METHODSTwo test lungs are connected to a single ventilator using DuplicAR(R). Three experimental stages are established: 1) two identical subjects, 2) two subjects with the same weight but different lung compliance, and 3) two subjects with different weight and lung compliance. In each stage, the test lungs are ventilated in two ventilation modes. The positive-end expiratory pressure requirements are increased successively in one of the subjects. The goal is to achieve a tidal volume of 7 ml/kg for each subject in all different stages through manipulation of the ventilator and the DuplicAR(R) controllers. RESULTSDuplicAR(R) allows adequate ventilation of two subjects with different weight and/or lung compliance and/or PEEP requirements. This is achieved by adjusting the total tidal volume for both subjects (in volume-controlled ventilation) or the highest peak pressure needed (in pressure-controlled ventilation) along with the basal positive-end expiratory pressure on the ventilator, and simultaneously manipulating the DuplicAR(R) controllers to decrease the tidal volume or the peak pressure in the subject that needs less and/or to increase the positive-end expiratory pressure in the subject that needs more. While ventilatory goals can be achieved in any of the ventilation modes, DuplicAR(R) performs better in pressure-controlled ventilation, as changes experienced in the variables of one subject do not modify the other one. CONCLUSIONSDuplicAR(R) is an effective tool to manage the peak inspiratory pressure and the positive-end expiratory pressure independently in two subjects connected to a single ventilator. The driving pressure can be adjusted to meet the requirements of subjects with different weight and lung compliance. Pressure-controlled ventilation has advantages over volume-controlled ventilation and is therefore the recommended ventilation mode.

bioengineering↗

bioRxiv2019

Towards a gold standard for benchmarking gene set enrichment analysis

BackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.\n\nResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.\n\nConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.\n\nAvailabilityhttp://bioconductor.org/packages/GSEABenchmarkeR

bioinformatics↗

bioRxiv2019

Multi-omic analysis supports a developmental hierarchy of molecular subtypes in high-grade serous ovarian carcinoma

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but these have yet to impact clinical practice. Interpretation and translation of HGSOC subtypes are complicated by tumor evolution and polyclonality accompanied by accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. The chronology of HGSOC subtype evolution was examined in the context of these factors by a novel integrative analysis of bulk absolute somatic copy number analysis and gene expression in The Cancer Genome Atlas, complemented by single-cell RNA-seq analysis of six independent tumors. The approach was validated by contrast to soft-tissue sarcoma. Genomic lesions associated with HGSOC subtypes tend to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations is particularly evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors are characterized by largely intact genome integrity, high immune infiltration, and younger patient age. We propose an alternative model to discrete subtypes of HGSOC, in which tumors develop from an early differentiated spectrum to a late proliferative spectrum, along a timeline characterized by increasing genomic instability and subclonal expansion. The proposed methods provide a new approach to investigating tumor evolution through multi-omic analysis. Statement of SignificanceThis study proposes a method to infer whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are therefore potentially appropriate targets for therapy, and demonstrates that this is not the case for high-grade serous ovarian carcinoma (HGSOC). Significant findings for HGSOC include: O_LITumor purity, ploidy, and subclonality can be reliably inferred from different genomic platforms and show marked differences between subtypes C_LIO_LIRecurrent DNA alterations are associated with subtypes and tend to occur more frequently in subclones C_LIO_LISingle-cell sequencing of 42,000 tumor cells reveals widespread heterogeneity in tumor cell type composition that drives bulk subtype calls, but demonstrates a lack of intrinsic subtypes among tumor epithelial cells C_LIO_LIFindings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tissue of origin and tumor stage, and evolution between properties previously associated with discrete subtypes C_LI

cancer biology↗