Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable hostgene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90-0.93) and a viral-vsother AUROC 0.92 (95% CI 0.90-0.93). We then apply this classifier, inflammatix-bacterialviral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77-0.93), and viral-vs.-other 0.85 (95% CI 0.76-0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83-0.99), and viral-vs.-other 0.91 (95% CI 0.82-0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Summary Proper cell size is essential for cellular function. Nonetheless, despite more than 100 years of work on the subject, the mechanisms that maintain cell size homeostasis are largely mysterious [1]. Cells in growing populations maintain cell size within a narrow range by coordinating growth and division. Bacterial and eukaryotic cells both demonstrate homeostatic size control, which maintains population-level variation in cell size within a certain range, and returns the population average to that range if it is perturbed [1, 2]. Recent work has proposed two different strategies for size control: budding yeast has been proposed to use an inhibitor-dilution strategy to regulate size at the G1/S transition [3], while bacteria appear to use an adder strategy, in which a fixed amount of growth each generation causes cell size to converge on a stable average [4-6]. Here we present evidence that cell size in the fission yeast Schizosaccharomyces pombe is regulated by a third strategy: the size dependent expression of the mitotic activator Cdc25. The cdc25 transcript levels are regulated such that smaller cells express less Cdc25 and larger cells express more Cdc25, creating an increasing concentration of Cdc25 as cell grow and providing a mechanism for cell to trigger cell division when they reach a threshold concentration of Cdc25. Since regulation of mitotic entry by Cdc25 is well conserved, this mechanism may provide a wide spread solution to the problem of size control in eukaryotes.

Summary During embryonic cell cycles, B-cyclin-CDKs function as the core component of an autonomous oscillator. Current models for the cell-cycle oscillator in non-embryonic cells are slightly more complex, incorporating multiple G1, S-phase, and mitotic cyclin-CDK complexes. However, periodic events persist in yeast cells lacking all S-phase and mitotic B-cyclin genes, challenging the assertion that cyclin-CDK complexes are essential for oscillations. These and other results led to the proposal that a network of sequentially activated transcription factors functions as an underlying cell-cycle oscillator. Here we examine the individual contributions of a transcription-factor network and cyclin-CDKs to the maintenance of cell-cycle oscillations. Our findings suggest that while cyclin-CDKs are not required for oscillations, they do contribute to oscillation robustness. A model emerges in which cyclin expression (thereby, CDK activity) is entrained to an autonomous transcriptional oscillator. CDKs then modulate oscillator function and serve as effectors of the oscillator.

In our mathematical model of a transcription factor network, we inadvertently reported the wrong logic rules for the TF, YHP1 in Figure 3A. In each column (A-D), the logic should read, ''MCM1 n SBF n HCM1 ^: ASH1'' (instead of ''n : ASH1''). Note also that activation edges from MCM and HCM1 to YHP1 were unintentionally omitted from the network graphs depicted in Figures 2J and S2A. Finally, in Figure 3C, the distributions for the attractors using activation logic ''D'' should be 35.94%, 64.06%, and 0.00%, respectively, for the attractors 1, 2, and 3.The updated versions of the figures are provided below. We apologize for any confusion these errors may have caused.

BackgroundExamination of complex biological systems has long been achieved through methodical investigation of the system’s individual components. While informative, this strategy often leads to inappropriate conclusions about the system as a whole. With the advent of high-throughput “omic” technologies, however, researchers can now simultaneously analyze an entire system at the level of molecule (DNA, RNA, protein, metabolite) and process (transcription, translation, enzyme catalysis). This strategy reduces the likelihood of improper conclusions, provides a framework for elucidation of genotype-phenotype relationships, and brings finer resolution to comparative genomic experiments. Here, we apply a multi-omic approach to analyze the gene expression profiles of two closely related Pseudomonas aeruginosa strains grown in n-alkanes or glycerol.ResultsThe environmental P. aeruginosa isolate ATCC 33988 consumed medium-length (C10–C16) n-alkanes more rapidly than the laboratory strain PAO1, despite high genome sequence identity (average nucleotide identity >99%). Our data shows that ATCC 33988 induces a characteristic set of genes at the transcriptional, translational and post-translational levels during growth on alkanes, many of which differ from those expressed by PAO1. Of particular interest was the lack of expression from the rhl operon of the quorum sensing (QS) system, resulting in no measurable rhamnolipid production by ATCC 33988. Further examination showed that ATCC 33988 lacked the entire lasI/lasR arm of the QS response. Instead of promoting expression of QS genes, ATCC 33988 up-regulates a small subset of its genome, including operons responsible for specific alkaline proteases and sphingosine metabolism.ConclusionThis work represents the first time results from RNA-seq, microarray, ribosome footprinting, proteomics, and small molecule LC-MS experiments have been integrated to compare gene expression in bacteria. Together, these data provide insights as to why strain ATCC 33988 is better adapted for growth and survival on n-alkanes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3708-4) contains supplementary material, which is available to authorized users.