Marine microeukaryotes express large and complex transcriptomes that often respond dynamically to environmental and physiological conditions. In parallel to developments in human disease research, the opportunity exists to employ transcriptomic features as "biomarkers" to understand and predict cellular and environmental states. Here, the prediction and classification of basic physiological and environmental states including light, growth phase and inorganic carbon status was explored for the model diatom T. pseudonana using publicly available data including 56 microarray and 316 mRNA-seq samples. Simple "machine learning" methods combined with integrative bootstrapped clustering were able to detect, recapitulate and expand biologically and environmentally relevant signals evident across hundreds of samples collected and processed independently by multiple laboratories. Agnostic, integrative and empirical "data-driven" approaches are likely applicable to modern questions in new environmental and experimental contexts.

In this study we investigated the effect of light-limitation (∼20 μmol photons m-2 s-1) on the southern hemisphere seagrass, Zostera muelleri. RNA sequencing, chlorophyll fluorometry and HPLC techniques were used to investigate how the leaf-specific transcriptome drives changes in photosynthesis and photo-pigments in Z. muelleri over 6 days. 1593 (7.51%) genes were differentially expressed on day 2 and 1481 (6.98%) genes were differentially expressed on day 6 of the experiment. Differential gene expression correlated with significant decreases in rETRMax, Ik, an increase in Yi (initial photosynthetic quantum yield of photosystem II), and significant changes in pigment composition. Regulation of carbohydrate metabolism was observed along with evidence that abscisic acid may serve a role in the low-light response of this seagrass. This study provides a novel understanding of how Z. muelleri responds to light-limitation in the marine water column and provides potential molecular markers for future conservation monitoring efforts.

Currently, the best clinical predictor for inflammatory bowel disease (IBD) is family history. Over 163 sequence variants have been associated with IBD in genome-wide association studies, but they have weak effects and explain only a fraction of the observed heritability. It is expected that additional variants contribute to the genomic architecture of IBD, possibly including rare variants with effect sizes larger than the identified common variants. Here we applied a family study design and sequenced 38 individuals from five families, under the hypothesis that families with multiple IBD-affected individuals harbor one or more risk variants that (i) are shared among affected family members, (ii) are rare and (iii) have substantial effect on disease development. Our analysis revealed not only novel candidate risk variants but also high polygenic risk scores for common known risk variants in four out of the five families. Functional analysis of our top novel variant in the remaining family, a rare missense mutation in the ubiquitin ligase TRIM11, suggests that it leads to increased nuclear factor of kappa light chain enhancer in B-cells (NF-κB) signaling. We conclude that an accumulation of common weak-effect variants accounts for the high incidence of IBD in most, but not all families we analyzed and that a family study design can identify novel rare variants conferring risk for IBD with potentially large effect size, such as the TRIM11 p.H414Y mutation.

BACKGROUND: Expansion of transcription factors is believed to have played a crucial role in evolution of all organisms by enabling them to deal with dynamic environments and colonize new environments. We investigated how the expansion of the Feast/Famine Regulatory Protein (FFRP) or Lrp-like proteins into an eight-member family in Halobacterium salinarum NRC-1 has aided in niche-adaptation of this archaeon to a complex and dynamically changing hypersaline environment. RESULTS: We mapped genome-wide binding locations for all eight FFRPs, investigated their preference for binding different effector molecules, and identified the contexts in which they act by analyzing transcriptional responses across 35 growth conditions that mimic different environmental and nutritional conditions this organism is likely to encounter in the wild. Integrative analysis of these data constructed an FFRP regulatory network with conditionally active states that reveal how interrelated variations in DNA-binding domains, effector-molecule preferences, and binding sites in target gene promoters have tuned the functions of each FFRP to the environments in which they act. We demonstrate how conditional regulation of similar genes by two FFRPs, AsnC (an activator) and VNG1237C (a repressor), have striking environment-specific fitness consequences for oxidative stress management and growth, respectively. CONCLUSIONS: This study provides a systems perspective into the evolutionary process by which gene duplication within a transcription factor family contributes to environment-specific adaptation of an organism.

Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein-DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.

© Springer International Publishing AG 2017. Marine biological systems are vast, productive, long-lived, dynamic, and complex. The oceans are fed and balanced primarily through photosynthesis and nutrient cycling by ubiquitous marine microalgae, including cyanobacteria, found thriving even in vast nutrient-limited pelagic deserts, and larger eukaryotic phytoplankton, whose genetic and functional diversity appear substantially more complex and understudied than initially expected. The rapid complete sequencing of environmentally relevant genomes has provided the first precise molecular descriptions of complete biological systems. Metatranscriptomic data quickly and easily provide intercomparable system-wide and conditionally relevant functional information. Environmental proteomics are able to directly identify functional protein biomarkers of nutrient conditions. The integration of comprehensive molecular data (genomes, transcriptomes, proteomes, metabolomes) into models capable of rigorous hypothesis testing and prediction constitutes a new way to study the connections between genotype and phenotype, phenotype and environment, species and ecosystems, and interspecies evolution and adaptation. To date, marine microalgae are the first and most extensively studied marine organisms in terms of their functioning as coalescent molecular systems. The richness of data, systematic integration, and predictive models therein set a new example for the broad new study of marine life at unprecedented detail and comparability, promising answers to broad new scientific, statistical, and quantitative questions of critical concern for the present and future functioning and adaptability of the world's oceans.

Abstract Eukaryotic microalgae dominate primary photosynthetic productivity in fluctuating nutrient-rich environments, including coastal, estuarine and polar regions, where competition and complexity are presumably adaptive and dynamic traits. Numerous genomes and transcriptomes of these species have been carefully sequenced, providing an unprecedented view into the vast genetic repertoires and the diverse transcriptional programs operating inside these organisms. Here we collected, re-mapped, quantified and clustered publicly available transcriptome data for ten different eukaryotic microalgae in order to develop new insights into their molecular systems biology, as well as to provide a large new resource of integrated information to facilitate the efforts of others to further compare and contextualize the results of individual and new experiments within and between species. This is summarized herein and provided for public use by the eukaryotic microalgae research community.