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Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus-its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and "foreman" TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.[Supplemental material is available for this article.]In this paper we present an efficient means of comprehensively mapping the network of transcription factors (TFs) that regulate a particular physiological process. Our approach cycles through deletion of TFs, expression profiling of TF mutants, model construction, and model-directed selection of TFs for the next round of deletion. This predictive genetics approach identifies TFs that affect the process of interest, providing a valuable complement to undirected mutagenesis and screening. Simultaneously, it builds a network model that explains how the TFs affect the process, yielding novel insights into the biological system under study.Mapping the network that regulates a specific process requires knowing which TFs affect that process. One way to identify such TFs is to screen comprehensive mutant libraries, but generating such libraries is not always feasible. Furthermore, genome-scale screening assays must be fast and scalable; such assays may not exist for the process of interest or may be less sensitive than other, more laborious assays. An alternative approach is to map the targets of all TFs encoded in a genome by using methods such as chromatin-immunoprecipitation (ChIP) or large-scale TF deletion and expression analysis. However, undirected, genome-wide approaches are costly and inefficient for probing a specific biological process in detail. We report a model-guided approach that addresses all of these problems by focusing experimental effort on the TFs most likely to be involved in the process of interest. Furthermore, our approach generates a network that provides mechanistic explanations for the phenotypes of TF deletion mutants.Our approach alternates network building by using an algorithm we call NetProphet with identifying relevant TFs by using an algorithm we call PhenoProphet. NetProphet is a validated method for mapping direct, functional regulation that significantly out...

A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein–DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.

SummaryThe precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Cryptococcus neoformans is a ubiquitous, opportunistic fungal pathogen that kills over 600,000 people annually. Here, we report integrated computational and experimental investigations of the role and mechanisms of transcriptional regulation in cryptococcal infection. Major cryptococcal virulence traits include melanin production and the development of a large polysaccharide capsule upon host entry; shed capsule polysaccharides also impair host defenses. We found that both transcription and translation are required for capsule growth and that Usv101 is a master regulator of pathogenesis, regulating melanin production, capsule growth, and capsule shedding. It does this by directly regulating genes encoding glycoactive enzymes and genes encoding three other transcription factors that are essential for capsule growth: GAT201, RIM101, and SP1. Murine infection with cryptococci lacking Usv101 significantly alters the kinetics and pathogenesis of disease, with extended survival and, unexpectedly, death by pneumonia rather than meningitis. Our approaches and findings will inform studies of other pathogenic microbes.

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genomewide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.gene-regulatory networks | regulatory systems biology | transcriptome | engineering | Saccharomyces cerevisiae T he central premise of regulatory systems biology is that a systematic map of a cell's regulatory machinery will enable us to understand, predict, and rationally manipulate the cell's state or behavior. Manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and gene therapy. Progress toward cellular state control has been driven by both the systems biology and the synthetic biology research communities. Systems biology has produced whole-genome regulatory network maps (1), but relatively little research has focused on using these maps for predicting and manipulating cellular behavior (2). Regulatory synthetic biology has focused on creating molecular circuits that can be placed into a cell to control the transcription of a small number of transgenes, but genome-scale engineering of the cell's native regulatory apparatus is still rare, with most systems restricted to a limited set of controlled targets (3). Here, we demonstrate that transcription factor (TF) network mapping, gene expression profiling, and computational modeling can be integrated to rationally engineer transcriptional state. We call this activity, which bridges the gap between systems biology and synthetic biology, "transcriptome engineering

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Introduction The coronavirus disease 2019 (COVID-19) pandemic is a global public health emergency causing a disparate burden of death and disability around the world. The molecular characteristics of the virus that predict better or worse outcome are largely still being discovered. Methods We downloaded 155,958 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from GISAID. Of these genomes, 3,637 samples included useable metadata on patient outcomes. Using this subset, we evaluated whether SARS-CoV-2 viral genomic variants improved prediction of reported severity beyond age and region. First, we established whether including genomic variants as model features meaningfully increased predictive power of our model. Next, we evaluated specific variants in order to determine the magnitude of association with severity and the frequency of these variants among SARS-CoV-2 genomes. Results Logistic regression models that included viral genomic variants outperformed other models (AUC = 0.91 as compared with 0.68 for age and gender alone; p < 0.001). Among individual variants, we found 17 single nucleotide variants in SARS-CoV-2 have more than two-fold greater odds of being associated with higher severity and 67 variants associated with ≤0.5 times the odds of severity. The median frequency of associated variants was 0.15% (interquartile range 0.09%-0.45%). Altogether 85% of genomes had at least one variant associated with patient outcome. Conclusion Numerous SARS-CoV-2 variants have two-fold or greater association with odds of mild or severe outcome and collectively, these variants are common. In addition to comprehensive mitigation efforts, public health measures should be prioritized to control the more severe manifestations of COVID-19 and the transmission chains linked to these severe cases. Lay Summary This study explores which, if any, SARS-CoV-2 viral genomic variants are associated with mild or severe COVID-19 patient outcomes. Our results suggest that there are common genomic variants in SARS-CoV-2 that are more often associated with negative patient outcomes, which may impact downstream public health measures.