From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.

Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inherent noise in the data. Here we introduce RegDiffusion, a new class of Denoising Diffusion Probabilistic Models focusing on the regulatory effects among feature variables.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs.

hnRNPL expression dynamics in the embryo and placenta.

Heterogeneous nuclear ribonucleoprotein L (hnRNPL) is a conserved RNA binding protein (RBP) that plays an important role in the alternative splicing of gene transcripts, and thus in the generation of specific protein isoforms. Global deficiency in hnRNPL in mice results in preimplantation embryonic lethality at embryonic day (E) 3.5.

Cell-specific imputation of drug connectivity mapping with incomplete data.

Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease's impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing.

Intergenerational effects of preconception opioids on glucose homeostasis and hepatic transcription in adult male rats.

Adolescence represents a period of significant neurodevelopment during which adverse experiences can lead to prolonged effects on disease vulnerability, including effects that can impact future offspring. Adolescence is a common period for the initiation of drug use, including the use of opioids. Beyond effects on central reward, opioids also impact glucose metabolism, which can impact the risk of diabetes.

hnRNP L is essential for myogenic differentiation and modulates myotonic dystrophy pathologies.

Introduction: RNA-binding proteins (RBPs) play an important role in skeletal muscle development and disease by regulating RNA splicing. In myotonic dystrophy type 1 (DM1), the RBP MBNL1 (muscleblind-like) is sequestered by toxic CUG repeats, leading to missplicing of MBNL1 targets. Mounting evidence from the literature has implicated other factors in the pathogenesis of DM1.

aTEMPO: Pathway-Specific Temporal Anomalies for Precision Therapeutics.

Dynamic processes are inherently important in disease, and identifying disease-related disruptions of normal dynamic processes can provide information about individual patients. We have previously characterized individuals' disease states via pathway-based anomalies in expression data, and we have identified disease-correlated disruption of predictable dynamic patterns by modeling a virtual time series in static data. Here we combine the two approaches, using an anomaly detection model and virtual time series to identify anomalous temporal processes in specific disease states.

A history of opioid exposure in females increases the risk of metabolic disorders in their future male offspring.

Worldwide consumption of opioids remains at historic levels. Preclinical studies report intergenerational effects on the endogenous opioid system of future progeny following preconception morphine exposure. Given the role of endogenous opioids in energy homeostasis, such effects could impact metabolism in the next generation.

Assessment of network module identification across complex diseases.

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks.

Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease.

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We define the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes.

Putting benchmarks in their rightful place: The heart of computational biology.

Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, as essentially all publications introducing a new method will claim better performance than all others. Not all of these claims can be correct.

Towards a more molecular taxonomy of disease.

Background: Disease taxonomies have been designed for many applications, but they tend not to fully incorporate the growing amount of molecular-level knowledge of disease processes, inhibiting research efforts. Understanding the degree to which we can infer disease relationships from molecular data alone may yield insights into how to ultimately construct more modern taxonomies that integrate both physiological and molecular information.

Results: We introduce a new technique we call Parent Promotion to infer hierarchical relationships between disease terms using disease-gene data.

Amniotic fluid transcriptomics reflects novel disease mechanisms in fetuses with myelomeningocele.

Background: Cell-free RNA in amniotic fluid supernatant reflects developmental changes in gene expression in the living fetus, which includes genes that are specific to the central nervous system. Although it has been previously shown that central nervous system-specific transcripts are present in amniotic fluid supernatant, it is not known whether changes in the amniotic fluid supernatant transcriptome reflect the specific pathophysiologic condition of fetal central nervous system disorders. In myelomeningocele, there is open communication between the central nervous system and amniotic fluid.

Using Next-Generation Sequencing to Explore Genetics and Race in the High School Classroom.

With the development of new sequencing and bioinformatics technologies, concepts relating to personal genomics play an increasingly important role in our society. To promote interest and understanding of sequencing and bioinformatics in the high school classroom, we developed and implemented a laboratory-based teaching module called "The Genetics of Race." This module uses the topic of race to engage students with sequencing and genetics.

Personal microbiomes and next-generation sequencing for laboratory-based education.

Sequencing and bioinformatics technologies have advanced rapidly in recent years, driven largely by developments in next-generation sequencing (NGS) technology. Given the increasing importance of these advances, there is a growing need to incorporate concepts and practices relating to NGS into undergraduate and high school science curricula. We believe that direct access to sequencing and bioinformatics will improve the ability of students to understand the information obtained through these increasingly ubiquitous research tools.

Time is Money: Designing Cost-Effective Time Series Experiments.

A new theoretical model helps to evaluate the tradeoffs between running technical replicates in high-throughput experiments and sampling at more time points.

The fetal brain transcriptome and neonatal behavioral phenotype in the Ts1Cje mouse model of Down syndrome.

Human fetuses with Down syndrome demonstrate abnormal brain growth and reduced neurogenesis. Despite the prenatal onset of the phenotype, most therapeutic trials have been conducted in adults. Here, we present evidence for fetal brain molecular and neonatal behavioral alterations in the Ts1Cje mouse model of Down syndrome.

The pathway not taken: understanding 'omics data in the perinatal context.

Objective: 'Omics analysis of large datasets has an increasingly important role in perinatal research, but understanding gene expression analyses in the fetal context remains a challenge. We compared the interpretation provided by a widely used systems biology resource (ingenuity pathway analysis [IPA]) with that from gene set enrichment analysis (GSEA) with functional annotation curated specifically for the fetus (Developmental FunctionaL Annotation at Tufts [DFLAT]).

Study Design: Using amniotic fluid supernatant transcriptome datasets previously produced by our group, we analyzed 3 different developmental perturbations: aneuploidy (Trisomy 21 [T21]), hemodynamic (twin-twin transfusion syndrome [TTTS]), and metabolic (maternal obesity) vs sex- and gestational age-matched control subjects.

CSAX: Characterizing Systematic Anomalies in eXpression Data.

Methods for translating gene expression signatures into clinically relevant information have typically relied upon having many samples from patients with similar molecular phenotypes. Here, we address the question of what can be done when it is relatively easy to obtain healthy patient samples, but when abnormalities corresponding to disease states may be rare and one-of-a-kind. The associated computational challenge, anomaly detection, is a well-studied machine-learning problem.