Search bioRxiv⌕ Search

Biology subjects

Mias, G. I.

Publications and source records attributed to Mias, G. I..

16 recordsLinked to original sources

Integrated RNA sequencing reanalysis reveals reproducible matrix-immune signatures in idiopathic pulmonary fibrosis

In idiopathic pulmonary fibrosis (IPF), the lung is remodeled through coordinated epithelial, stromal, and immune-associated programs, but individual transcriptomic cohorts are often too small to separate shared disease signals from demographic and study-level variation. To increase statistical power while preserving study-aware interpretation, we integrated raw bulk lung RNA sequencing (RNA-seq) data from five well-annotated studies and analyzed 223 samples in a common framework that modeled sex, age, library layout and repeated sampling. IPF showed a broad and reproducible expression shift, with 2,443 genes meeting the differential-expression threshold of false discovery rate (FDR) < 0.05 and absolute log2 fold change at least 1. The dominant program combined extracellular matrix remodeling, stromal and epithelial activation, complement and B-cell-related pathways, cilium-associated processes, and relative depletion of oxidative phosphorylation and proteasome pathways. Sex-stratified analyses recovered a shared fibrotic core with smaller sex-skewed components, whereas age-related disease effects were weaker and centered on immune activation. A leave-one-study-out elastic-net analysis using fixed disease-gene panels classified IPF across held-out studies, supporting cross-study portability of the core signature. This integrated reanalysis strengthens evidence for a stable matrix-immune IPF program and reinforces the view that core disease-associated transcriptional programs are reproducible across heterogeneous cohorts.

genomics↗

Histone Demethylase KDM6B Promotes Postnatal Oligodendrocyte Development and Cortical Myelination

Myelination in the postnatal cortex requires epigenetic programs that activate oligodendrocyte gene networks. Here we show that the histone H3K27me3 demethylase KDM6B promotes this process in vivo. Conditional Kdm6b deletion (Kdm6b-cKO) in Emx1 dorsal telencephalic progenitors caused delayed cortical myelination. Lineage-specific RNA-seq revealed that the oligodendrocyte master regulator Sox10 was significantly reduced, as well as myelination effectors, indicating impaired oligodendrocyte maturation. Chromatin immunoprecipitation assays of the same lineage showed increased H3K27me3 at promoter-proximal regions of Sox10, consistent with loss of KDM6B-mediated de-repression. Temporally controlled Sox10 induction partially rescued myelination in Kdm6b-cKO mice. These findings define a KDM6B[->]SOX10 axis that licenses oligodendrocyte development and cortical myelination by removing repressive H3K27me3 and sustaining active chromatin, with implications for KDM6B-related neurodevelopmental disorders.

neuroscience↗

SOX2 is a dispensable modulator of NUT carcinoma oncogenesis in mouse

NUT carcinoma (NC) is a highly aggressive malignancy driven by BRD4::NUTM1 and other NUTM1 fusion oncogenes. BRD4::NUTM1 aberrantly activates transcription factors (TFs) linked to basal progenitor identity, producing poorly differentiated squamous phenotypes. Among these TFs, SOX2 has been proposed as a critical oncogenic driver, but its functional requirement in NC has not been tested in vivo. Using a genetically engineered mouse model that faithfully recapitulates human NC, we performed lineage-specific conditional deletion of Sox2 in both squamous and non-squamous tissues. We found that Sox2 is dispensable for NC initiation and progression, with tumors retaining characteristic histology and expression of key drivers including BRD4::NUTM1, MYC, and TP63. Transcriptomic profiling revealed only modest changes in Sox2-deficient tumors, mainly affecting metabolic and biosynthetic pathways, without disrupting core oncogenic programs. These findings challenge the assumption that SOX2 is universally required in NC and suggest that SOX2-targeted therapies may have limited utility, refining the framework for therapeutic prioritization. Summary blurbThis study shows that SOX2 is not required for NUT carcinoma initiation or maintenance in vivo, challenging its assumed oncogenic role and refining therapeutic target prioritization.

cancer biology↗

Time-resolved molecular measurements reveal changes in astronauts during spaceflight

From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n=27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.

molecular biology↗

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Longitudinal deep multi-omics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we propose a bottom-up framework starting from assessing single individuals multi-omics time series, and using individual responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multi-omics profiles. We applied our method to individual profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results showed that our method identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which was associated to measures of their diabetic status.

genomics↗

Differential Network Analysis of Longitudinal Gene Expression in Response to Perturbations.

Understanding changes in gene expression under the effects of a perturbation is a key goal of systems biology. A powerful approach to address this goal uses gene networks and describes the perturbations effects as a rewiring of each genes connections. This approach is known as differential network (DN) analysis. Here, we used DNs to analyze RNA-sequencing time series datasets, focusing on expression changes: (i) In the saliva of a human subject after vaccination with a pneumococcal vaccine (PPSV23), and (ii) in B cells treated ex vivo with a monoclonal antibody drug (Rituximab). Using network community detection, we revealed the collective behavior of clusters of genes, and detected communities of genes based on their longitudinal behavior, and corresponding pathway activations. We identified biological pathways consistent with the mechanism of action of the vaccine and with Rituximabs targets. The approach may be useful in drug development by providing an effective analysis of expressing changes in response to a drug.

bioinformatics↗

Impaired KDM2B-mediated PRC1 recruitment to chromatin causes neural stem cell senescence and ASD/ID-like behavioral deficits

Autism spectrum disorder (ASD) and intellectual disability (ID) are neurodevelopmental diseases associated with various genetic mutations. Recent clinical studies report that chromosomal 12q24.31 microdeletions are associated with human ASD/ID. However, the causality and underlying mechanisms linking 12q24.31 microdeletions to ASD/ID pathogenesis remain undetermined. Here we show Kdm2b, one of the genes located in chromosomal 12q24.31, plays a critical role in maintaining neural stem cells (NSCs) in the developing mouse brain. Loss of the CxxC-ZF domain of KDM2B impairs its function in recruiting Polycomb repressive complex 1 (PRC1) to chromatin, resulting in de-repression of genes involved in cell apoptosis, cell cycle arrest, NSC premature senescence, and leading to the loss of NSC populations in the brain. Importantly, the Kdm2b mutation is sufficient to induce ASD/ID-like social and memory deficits in adult mice. Thus, our study reveals a critical role of an epigenetic factor KDM2B in normal brain development, a causality between the Kdm2b mutation and genesis of ASD/ID-like phenotypes in mice, and potential molecular mechanisms linking the function of KDM2B-PRC1 in transcriptional regulation and NSC senescence to the12q24.31 microdeletion-associated ASD/ID.

neuroscience↗

Histone H3K36me2-specific methyltransferase ASH1L is required for the MLL-AF9-induced leukemogenesis

ASH1L and MLL1 are two histone methyltransferases that facilitate transcriptional activation during normal development. However, the roles of ASH1L and its enzymatic activity in the development of MLL-rearranged leukemias are not fully elucidated in the Ash1L gene knockout animal models. In this study, we used an Ash1L conditional knockout mouse model to show that loss of ASH1L in hematopoietic progenitor cells impaired the initiation of MLL-AF9-induced leukemic transformation in vitro. Furthermore, genetic deletion of ASH1L in the MLL-AF9-transformed cells impaired the maintenance of leukemic cells in vitro and largely blocked the leukemia progression in vivo. Importantly, the loss of ASH1L function in the Ash1L-deleted cells could be rescued by wild-type but not the catalytic-dead mutant ASH1L, suggesting the enzymatic activity of ASH1L was required for its function in promoting MLL-AF9-induced leukemic transformation. At the molecular level, ASH1L enhanced the MLL-AF9 target gene expression by directly binding to the gene promoters and modifying the local histone H3K36me2 levels. Thus, our study revealed the critical functions of ASH1L in promoting the MLL-AF9-induced leukemogenesis, which provides a molecular basis for targeting ASH1L and its enzymatic activity to treat MLL-arranged leukemias.

cancer biology↗

Loss of ASH1L in developing brains causes autistic-like behaviors in a mouse model

Autism spectrum disorder (ASD) is a neurodevelopmental disease associated with various gene mutations. Recent genetic and clinical studies report that mutations of the epigenetic gene ASH1L are highly associated with human ASD and intellectual disability (ID). However, the causal link between ASH1L mutations and ASD/ID remains undetermined. Here we show loss of ASH1L in developing mouse brains is sufficient to cause multiple developmental defects, core autistic-like behaviors, and impaired cognitive memory. Gene expression analyses uncover critical roles of ASH1L in regulating gene expression during neural cell development. Thus, our study establishes a new ASD/ID mouse model revealing the critical function of ASH1L in normal brain development, a causality between Ash1L mutations and ASD/ID-like behaviors in mice, and potential molecular mechanisms linking Ash1L mutations to brain functional abnormalities.

neuroscience↗

Longitudinal Saliva Omics Responses to Immune Perturbation: A Case Study

Saliva omics, a rapidly developing field for non-invasive diagnostics, may be utilized for monitoring very young or elderly populations, as well as individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over 100 timepoints, over three periods involving: (i) hourly sampling over 24 hours without intervention, (ii) hourly sampling over 24 hours including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (iii) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome were profiled, and salivary extracellular vesicles were derived, from which small-RNA sequencing was used to determine RNA, miRNA, piRNA and bacterial RNA components. The two 24-hour periods were used in a paired analysis to reveal vaccination responses. Temporal trends were classified and collective behavior revealed broad immune-responses captured in saliva, both at the innate as well as the adaptive response time frames.

genomics↗

Visibility Graph Based Community Detection for Biological Time Series

MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks. ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns. AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984. Contactgmias@msu.edu

bioinformatics↗

PyIOmica: Longitudinal Omics Analysis and Classification

SummaryPyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing, and classifying temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, classification, visualization, and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks.\n\nAvailability and implementationPyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (PyPI) (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3342612).\n\nContactgmias@msu.edu

systems biology↗

Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls. We used pre-existing, publicly available microarray expression datasets to conduct a meta-analysis. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we ran an analysis of variance with a linear model with disease state, age, sex, smoking status and study as effects that also included binary interactions. We found 1,513 statistically significant (Benjamini-Hochberg-adjusted p-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from a Tukey test post-hoc analysis (Benjamini-Hochberg-adjusted p-value <0.05 and 10% two-tailed quantiles of mean differences between COPD and control), to identify 304 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the 304 genes as features, which enabled prediction of disease status with 84% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

molecular biology↗

FGF/MAPK signaling pathway regulates the genome-wide occupancy of Polycomb Repressive Complex 2 in murine embryonic stem cells

The recruitment of Polycomb repressive complex 2 (PRC2) to gene promoters is critical for its function in repressing gene expression in murine embryonic stem cells (mESCs). However, previous studies have demonstrated although the expression of early lineage-specific genes is largely repressed, the genome-wide PRC2 occupancy is unexpectedly reduced in naive mESCs. In this study, we provide evidence to show the FGF/ERK signaling determines the global PRC2 occupancy through regulating the expression of PRC2-recruting factor JARID2 in naive mESCs. At the transcriptional level, the de-repression of bivalent genes is predominantly determined by the presence of cell signaling-associated transcription factors but not the status of PRC2 occupancy at gene promoters. Hence, this study not only reveals a key molecular mechanism by which the FGF/ERK signaling in regulating the PRC2 occupancy in mESCs, but also elucidates a fundamental question regarding the functional roles of transcription factors and Polycomb-mediated epigenetic mechanisms in transcriptional regulation.

genetics↗

Stratified computational meta-analysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures

In 2018 alone, an estimated 20,000 new acute myeloid leukemia (AML) patients were diagnosed, in the United States, and over 10,000 of them are expected to die from the disease. AML is primarily diagnosed among the elderly (median 68 years old at diagnosis). Prognoses have significantly improved for younger patients, but in patients older than 60 years old as much as 70% of patients will die within a year of diagnosis. In this study, we conducted stratified computational meta-analysis of 2,213 acute myeloid leukemia patients compared to 548 healthy individuals, using curated publicly available data. We carried out analysis of variance of normalized batch corrected data, including considerations for disease, age, tissue and sex. We identified 974 differentially expressed probe sets and 4 significant pathways associated with AML. Additionally, we identified 70 sex- and 375 age-related probe set expression signatures relevant to AML. Finally, we used a machine learning model (KNN model) to classify AML patients compared to healthy individuals with 90+% achieved accuracy. Overall our findings provide a new reanalysis of public datasets, that enabled the identification of potential new gene sets relevant to AML that can potentially be used in future experiments and possible stratified disease diagnostics.

bioinformatics↗

ClassificaIO: machine learning for classification graphical user interface

Machine learning methods are being used routinely by scientists in many research areas, typically requiring significant statistical and programing knowledge. Here we present ClassificaIO, an open-source Python graphical user interface for machine learning classification for the scikit-learn Python library. ClassificaIO provides an interactive way to train, validate, and test data on a range of classification algorithms. The software enables fast comparisons within and across classifiers, and facilitates uploading and exporting of trained models, and both validation and testing data results. ClassificaIO aims to provide not only a research utility, but also an educational tool that can enable biomedical and other researchers with minimal machine learning background to apply machine learning algorithms to their research in an interactive point-and-click way. The ClassificaIO package is available for download and installation through the Python Package Index (PyPI) (http://pypi.python.org/pypi/ClassificaIO) and it can be deployed using the \"import\" function in Python once the package is installed. The application is distributed under an MIT license and the source code is publicly available for download (for Mac OS X, Linux and Microsoft Windows) through PyPI and GitHub (http://github.com/gmiaslab/ClassificaIO, and https://doi.org/10.5281/zenodo.1320465).

bioinformatics↗