Deciphering eukaryotic gene-regulatory logic with 100 million random promotersThe MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters.

Predicting how transcription factors (TFs) interpret regulatory sequences to control gene expression remains a major challenge. Past studies have primarily focused on native or engineered sequences, and thus remained limited in scale. Here, we use random sequences as an alternative, measuring the expression output of nearly 100 million synthetic yeast promoters comprised of random DNA. Random sequences yield a broad range of reproducible expression levels, indicating that the fortuitous binding sites in random DNA are functional. From this data we learn 'billboard' models of transcriptional regulation that explain 93% of expression variation of test data, recapitulate the organization of native chromatin in yeast, and help refine cis-regulatory motifs. Analyzing the residual variation, we uncover more complex regulatory mechanisms, such as strand, position, and helical face preferences of TFs. Such high-throughput regulatory assays of random DNA provide the large-scale data necessary to learn complex models of cis-regulatory logic.

Candida albicans occupies diverse ecological niches within the host and must tolerate a wide range of environmental pH. The plasma membrane H + -ATPase Pma1p is the major regulator of cytosolic pH in fungi. Pma1p extrudes protons from the cytosol to maintain neutral-to-alkaline pH and is a potential drug target due to its essentiality and fungal specificity. We characterized mutants in which one allele of PMA1 has been deleted and the other truncated by 18–38 amino acids. Increasing C-terminal truncation caused corresponding decreases in plasma membrane ATPase-specific activity and cytosolic pH. Pma1p is regulated by glucose: glucose rapidly activates the ATPase, causing a sharp increase in cytosolic pH. Increasing Pma1p truncation severely impaired this glucose response. Pma1p truncation also altered cation responses, disrupted vacuolar morphology and pH, and reduced filamentation competence. Early studies of cytosolic pH and filamentation have described a rapid, transient alkalinization of the cytosol preceding germ tube formation; Pma1p has been proposed as a regulator of this process. We find Pma1p plays a role in the establishment of cell polarity, and distribution of Pma1p is non-homogenous in emerging hyphae. These findings suggest a role of PMA1 in cytosolic alkalinization and in the specialized form of polarized growth that is filamentation.

BackgroundInformation from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation.ObjectiveThis study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak.MethodsWe developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation.ResultsThe tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak.ConclusionsAIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approac...

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.