A transcriptomic atlas of acute stress response to low pH in multiple Issatchenkia orientalis strains.

IMPORTANCE: Issatchenkia orientalis is a promising industrial chassis to produce biofuels and bioproducts due to its high tolerance to multiple environmental stresses such as low pH, heat, and other chemicals otherwise toxic for the most widely used microbes. Yet, little is known about specific mechanisms of such tolerance in this organism, hindering our ability to engineer this species to produce valuable biochemicals. Here, we report a comprehensive study of the mechanisms of acidic tolerance in this species via transcriptome profiling across variable pH for 12 different strains with different phenotypes. We found multiple regulatory mechanisms involved in tolerance to low pH in different strains of I. orientalis, marking potential targets for future gene editing and perturbation experiments.

Influenza A virus infection disrupts oligodendrocyte homeostasis and alters the myelin lipidome in the adult mouse.

BACKGROUND: Recent data suggest that myelin may be altered by physiological events occurring outside of the central nervous system, which may cause changes to cognition and behavior. Similarly, peripheral infection by non-neurotropic viruses is also known to evoke changes to cognition and behavior. METHODS: Mice were inoculated with saline or influenza A virus. Bulk RNA-seq, lipidomics, RT-qPCR, flow cytometry, immunostaining, and western blots were used to determine the effect of infection on OL viability, protein expression and changes to the lipidome. To determine if microglia mediated infection-induced changes to OL homeostasis, mice were treated with GW2580, an inhibitor of microglia activation. Additionally, conditioned medium experiments using primary glial cell cultures were also used to test whether secreted factors from microglia could suppress OL gene expression. RESULTS: Transcriptomic and RT-qPCR analyses revealed temporal downregulation of OL-specific transcripts with concurrent upregulation of markers characteristic of cellular stress. OLs isolated from infected mice had reduced cellular expression of myelin proteins compared with those from saline-inoculated controls. In contrast, the expression of these proteins within myelin was not different between groups. Similarly, histological and immunoblotting analysis performed on various brain regions indicated that infection did not alter OL viability, but increased expression of a cellular stress marker. Shot-gun lipidomic analysis revealed that infection altered the lipid profile within the prefrontal cortex as well as in purified brain myelin and that these changes persisted after recovery from infection. Treatment with GW2580 during infection suppressed the expression of genes associated with glial activation and partially restored OL-specific transcripts to baseline levels. Finally, conditioned medium from activated microglia reduced OL-gene expression in primary OLs without altering their viability. CONCLUSIONS: These findings show that peripheral respiratory viral infection with IAV is capable of altering OL homeostasis and indicate that microglia activation is likely involved in the process.

Single-cell dissection of aggression in honeybee colonies.

Understanding how genotypic variation results in phenotypic variation is especially difficult for collective behaviour because group phenotypes arise from complex interactions among group members. A genome-wide association study identified hundreds of genes associated with colony-level variation in honeybee aggression, many of which also showed strong signals of positive selection, but the influence of these 'colony aggression genes' on brain function was unknown. Here we use single-cell (sc) transcriptomics and gene regulatory network (GRN) analyses to test the hypothesis that genetic variation for colony aggression influences individual differences in brain gene expression and/or gene regulation. We compared soldiers, which respond to territorial intrusion with stinging attacks, and foragers, which do not. Colony environment showed stronger influences on soldier-forager differences in brain gene regulation compared with brain gene expression. GRN plasticity was strongly associated with colony aggression, with larger differences in GRN dynamics detected between soldiers and foragers from more aggressive relative to less aggressive colonies. The regulatory dynamics of subnetworks composed of genes associated with colony aggression genes were more strongly correlated with each other across different cell types and brain regions relative to other genes, especially in brain regions involved with olfaction and vision and multimodal sensory integration, which are known to mediate bee aggression. These results show how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

CIMLA: Interpretable AI for inference of differential causal networks.

The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model estimates a causal quantity reflecting the influence of one variable on another, under certain assumptions. We leverage this insight to implement a new tool, CIMLA, for discovering condition-dependent changes in causal relationships. We then use CIMLA to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated data sets, we show that CIMLA is more robust to confounding variables and is more accurate than leading methods. Finally, we employ CIMLA to analyze a previously published single-cell RNA-seq data set collected from subjects with and without Alzheimer's disease (AD), discovering several potential regulators of AD.

The Liver X Receptor Is Selectively Modulated to Differentially Alter Female Mammary Metastasis-associated Myeloid Cells.

Dysregulation of cholesterol homeostasis is associated with many diseases such as cardiovascular disease and cancer. Liver X receptors (LXRs) are major upstream regulators of cholesterol homeostasis and are activated by endogenous cholesterol metabolites such as 27-hydroxycholesterol (27HC). LXRs and various LXR ligands such as 27HC have been described to influence several extra-hepatic biological systems. However, disparate reports of LXR function have emerged, especially with respect to immunology and cancer biology. This would suggest that, similar to steroid nuclear receptors, the LXRs can be selectively modulated by different ligands. Here, we use RNA-sequencing of macrophages and single-cell RNA-sequencing of immune cells from metastasis-bearing murine lungs to provide evidence that LXR satisfies the 2 principles of selective nuclear receptor modulation: (1) different LXR ligands result in overlapping but distinct gene expression profiles within the same cell type, and (2) the same LXR ligands differentially regulate gene expression in a highly context-specific manner, depending on the cell or tissue type. The concept that the LXRs can be selectively modulated provides the foundation for developing precision pharmacology LXR ligands that are tailored to promote those activities that are desirable (proimmune), but at the same time minimizing harmful side effects (such as elevated triglyceride levels).

An epigenomic shift in amygdala marks the transition to maternal behaviors in alloparenting virgin female mice.

Adults of many species will care for young offspring that are not their own, a phenomenon called alloparenting. However, in many cases, nonparental adults must be sensitized by repeated or extended exposures to newborns before they will robustly display parental-like behaviors. To capture neurogenomic events underlying the transition to active parental caring behaviors, we analyzed brain gene expression and chromatin profiles of virgin female mice co-housed with pregnant dams during pregnancy and after birth. After an initial display of antagonistic behaviors and a surge of defense-related gene expression, we observed a dramatic shift in the chromatin landscape specifically in amygdala of the pup-exposed virgin females compared to females co-housed with mother before birth, accompanied by a dampening of anxiety-related gene expression. This epigenetic shift coincided with hypothalamic expression of the oxytocin gene and the emergence of behaviors and gene expression patterns classically associated with maternal care. The results outline a neurogenomic program associated with dramatic behavioral changes and suggest molecular networks relevant to human postpartum mental health.

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer 'grammar' in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.

FASTRAL: improving scalability of phylogenomic analysis.

MOTIVATION: ASTRAL is the current leading method for species tree estimation from phylogenomic datasets (i.e. hundreds to thousands of genes) that addresses gene tree discord resulting from incomplete lineage sorting (ILS). ASTRAL is statistically consistent under the multi-locus coalescent model (MSC), runs in polynomial time, and is able to run on large datasets. Key to ASTRAL's algorithm is the use of dynamic programming to find an optimal solution to the MQSST (maximum quartet support supertree) within a constraint space that it computes from the input. Yet, ASTRAL can fail to complete within reasonable timeframes on large datasets with many genes and species, because in these cases the constraint space it computes is too large. RESULTS: Here, we introduce FASTRAL, a phylogenomic estimation method. FASTRAL is based on ASTRAL, but uses a different technique for constructing the constraint space. The technique we use to define the constraint space maintains statistical consistency and is polynomial time; thus we prove that FASTRAL is a polynomial time algorithm that is statistically consistent under the MSC. Our performance study on both biological and simulated datasets demonstrates that FASTRAL matches or improves on ASTRAL with respect to species tree topology accuracy (and under high ILS conditions it is statistically significantly more accurate), while being dramatically faster-especially on datasets with large numbers of genes and high ILS-due to using a significantly smaller constraint space. AVAILABILITYAND IMPLEMENTATION: FASTRAL is available in open-source form at https://github.com/PayamDiba/FASTRAL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks.

A common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic datasets that statistically resemble experimental data. However, most existing single-cell simulators do not incorporate transcription factor-gene regulatory interactions that underlie expression dynamics. Here, we present SERGIO, a simulator of single-cell gene expression data that models the stochastic nature of transcription as well as regulation of genes by multiple transcription factors according to a user-provided gene regulatory network. SERGIO can simulate any number of cell types in steady state or cells differentiating to multiple fates. We show that datasets generated by SERGIO are statistically comparable to experimental data generated by Illumina HiSeq2000, Drop-seq, Illumina 10X chromium, and Smart-seq. We use SERGIO to benchmark several single-cell analysis tools, including GRN inference methods, and identify Tcf7, Gata3, and Bcl11b as key drivers of T cell differentiation by performing in silico knockout experiments. SERGIO is freely available for download here: https://github.com/PayamDiba/SERGIO.

Global analysis of osteosarcoma lipidomes reveal altered lipid profiles in metastatic versus nonmetastatic cells.

Osteosarcoma (OS) is the most common form of primary bone cancer in humans. The early detection and subsequent control of metastasis has been challenging in OS. Lipids are important constituents of cells that maintain structural integrity that can be converted into lipid-signaling molecules and are reprogrammed in cancerous states. Here, we investigate the global lipidomic differences in metastatic (143B) and nonmetastatic (HOS) human OS cells as compared with normal fetal osteoblast cells (FOB) using lipidomics. We detect 15 distinct lipid classes in all three cell lines that included over 1,000 lipid species across various classes including phospholipids, sphingolipids and ceramides, glycolipids, and cholesterol. We identify a key class of lipids, diacylglycerols, which are overexpressed in metastatic OS cells as compared with their nonmetastatic or nontumorigenic counterparts. As a proof of concept, we show that blocking diacylglycerol synthesis reduces cellular viability and reduces cell migration in metastatic OS cells. Thus, the differentially regulated lipids identified in this study might aid in biomarker discovery, and the synthesis and metabolism of specific lipids could serve as future targets for therapeutic development.

DNA Origami-Graphene Hybrid Nanopore for DNA Detection.

DNA origami nanostructures can be used to functionalize solid-state nanopores for single molecule studies. In this study, we characterized a nanopore in a DNA origami-graphene heterostructure for DNA detection. The DNA origami nanopore is functionalized with a specific nucleotide type at the edge of the pore. Using extensive molecular dynamics (MD) simulations, we computed and analyzed the ionic conductivity of nanopores in heterostructures carpeted with one or two layers of DNA origami on graphene. We demonstrate that a nanopore in DNA origami-graphene gives rise to distinguishable dwell times for the four DNA base types, whereas for a nanopore in bare graphene, the dwell time is almost the same for all types of bases. The specific interactions (hydrogen bonds) between DNA origami and the translocating DNA strand yield different residence times and ionic currents. We also conclude that the speed of DNA translocation decreases due to the friction between the dangling bases at the pore mouth and the sequencing DNA strands.