The OsTIL1 lipocalin protects cell membranes from reactive oxygen species damage and maintains the 18:3-containing glycerolipid biosynthesis under cold stress in rice.

Lipocalins constitute a conserved protein family that binds to and transports a variety of lipids while fatty acid desaturases (FADs) are required for maintaining the cell membrane fluidity under cold stress. Nevertheless, it remains unclear whether plant lipocalins promote FADs for the cell membrane integrity under cold stress. Here, we identified the role of OsTIL1 lipocalin in FADs-mediated glycerolipid remodeling under cold stress. Overexpression and CRISPR/Cas9 mediated gene edition experiments demonstrated that OsTIL1 positively regulated cold stress tolerance by protecting the cell membrane integrity from reactive oxygen species damage and enhancing the activities of peroxidase and ascorbate peroxidase, which was confirmed by combined cold stress with a membrane rigidifier dimethyl sulfoxide or a H2 O2 scavenger dimethyl thiourea. OsTIL1 overexpression induced higher 18:3 content, and higher 18:3/18:2 and (18:2 + 18:3)/18:1 ratios than the wild type under cold stress whereas the gene edition mutant showed the opposite. Furthermore, the lipidomic analysis showed that OsTIL1 overexpression led to higher contents of 18:3-mediated glycerolipids, including galactolipids (monoglactosyldiacylglycerol and digalactosyldiacylglycerol) and phospholipids (phosphatidyl glycerol, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl serine and phosphatidyl inositol) under cold stress. RNA-seq and enzyme linked immunosorbent assay analyses indicated that OsTIL1 overexpression enhanced the transcription and enzyme abundance of four ω-3 FADs (OsFAD3-1/3-2, 7, and 8) under cold stress. These results reveal an important role of OsTIL1 in maintaining the cell membrane integrity from oxidative damage under cold stress, providing a good candidate gene for improving cold tolerance in rice.

DiseaseNet: a transfer learning approach to noncommunicable disease classification.

As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved.

SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm.

BACKGROUND: Genomic Islands (GIs) are clusters of genes that are mobilized through horizontal gene transfer. GIs play a pivotal role in bacterial evolution as a mechanism of diversification and adaptation to different niches. Therefore, identification and characterization of GIs in bacterial genomes is important for understanding bacterial evolution. However, quantifying GIs is inherently difficult, and the existing methods suffer from low prediction accuracy and precision-recall trade-off. Moreover, several of them are supervised in nature, and thus, their applications to newly sequenced genomes are riddled with their dependency on the functional annotation of existing genomes. RESULTS: We present SSG-LUGIA, a completely automated and unsupervised approach for identifying GIs and horizontally transferred genes. SSG-LUGIA is a novel method based on unsupervised anomaly detection technique, accompanied by further refinement using cues from signal processing literature. SSG-LUGIA leverages the atypical compositional biases of the alien genes to localize GIs in prokaryotic genomes. SSG-LUGIA was assessed on a large benchmark dataset `IslandPick' and on a set of 15 well-studied genomes in the literature and followed by a thorough analysis on the well-understood Salmonella typhi CT18 genome. Furthermore, the efficacy of SSG-LUGIA in identifying horizontally transferred genes was evaluated on two additional bacterial genomes, namely, those of Corynebacterium diphtheria NCTC13129 and Pseudomonas aeruginosa LESB58. SSG-LUGIA was examined on draft genomes and was demonstrated to be efficient as an ensemble method. CONCLUSIONS: Our results indicate that SSG-LUGIA achieved superior performance in comparison to frequently used existing methods. Importantly, it yielded a better trade-off between precision and recall than the existing methods. Its nondependency on the functional annotation of genomes makes it suitable for analyzing newly sequenced, yet uncharacterized genomes. Thus, our study is a significant advance in identification of GIs and horizontally transferred genes. SSG-LUGIA is available as an open source software at https://nibtehaz.github.io/SSG-LUGIA/.

Phytochrome B regulates reactive oxygen signaling during abiotic and biotic stress in plants.

Reactive oxygen species (ROS) and the photoreceptor protein phytochrome B (phyB) play a key role in plant acclimation to stress. However, how phyB that primarily functions in the nuclei impacts ROS signaling mediated by respiratory burst oxidase homolog (RBOH) proteins that reside on the plasma membrane, during stress, is unknown. Arabidopsis thaliana and Oryza sativa mutants, RNA-Seq, bioinformatics, biochemistry, molecular biology, and whole-plant ROS imaging were used to address this question. Here, we reveal that phyB and RBOHs function as part of a key regulatory module that controls apoplastic ROS production, stress-response transcript expression, and plant acclimation in response to excess light stress. We further show that phyB can regulate ROS production during stress even if it is restricted to the cytosol and that phyB, respiratory burst oxidase protein D (RBOHD), and respiratory burst oxidase protein F (RBOHF) coregulate thousands of transcripts in response to light stress. Surprisingly, we found that phyB is also required for ROS accumulation in response to heat, wounding, cold, and bacterial infection. Our findings reveal that phyB plays a canonical role in plant responses to biotic and abiotic stresses, regulating apoplastic ROS production, possibly while at the cytosol, and that phyB and RBOHD/RBOHF function in the same regulatory pathway.

Systemic signaling during abiotic stress combination in plants.

Extreme environmental conditions, such as heat, salinity, and decreased water availability, can have a devastating impact on plant growth and productivity, potentially resulting in the collapse of entire ecosystems. Stress-induced systemic signaling and systemic acquired acclimation play canonical roles in plant survival during episodes of environmental stress. Recent studies revealed that in response to a single abiotic stress, applied to a single leaf, plants mount a comprehensive stress-specific systemic response that includes the accumulation of many different stress-specific transcripts and metabolites, as well as a coordinated stress-specific whole-plant stomatal response. However, in nature plants are routinely subjected to a combination of two or more different abiotic stresses, each potentially triggering its own stress-specific systemic response, highlighting a new fundamental question in plant biology: are plants capable of integrating two different systemic signals simultaneously generated during conditions of stress combination? Here we show that plants can integrate two different systemic signals simultaneously generated during stress combination, and that the manner in which plants sense the different stresses that trigger these signals (i.e., at the same or different parts of the plant) makes a significant difference in how fast and efficient they induce systemic reactive oxygen species (ROS) signals; transcriptomic, hormonal, and stomatal responses; as well as plant acclimation. Our results shed light on how plants acclimate to their environment and survive a combination of different abiotic stresses. In addition, they highlight a key role for systemic ROS signals in coordinating the response of different leaves to stress.

CancerNet: a unified deep learning network for pan-cancer diagnostics.

BACKGROUND: Despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. Early detection of cancer and localization of the tissue of its origin are key to effective treatment. Here, we leverage technological advances in machine learning or artificial intelligence to design a novel framework for cancer diagnostics. Our proposed framework detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas (TCGA). Our model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, which arise early in carcinogenesis and differ remarkably between different cancer types or subtypes, thus holding a great promise in early cancer detection. RESULTS: Our comprehensive assessment of the proposed model on the 33 different tissues of origin demonstrates its ability to detect and classify cancers to a high accuracy (> 99% overall F-measure). Furthermore, our model distinguishes cancers from pre-cancerous lesions to metastatic tumors and discriminates between hypomethylation changes due to age related epigenetic drift and true cancer. CONCLUSIONS: Beyond detection of primary cancers, our proposed computational model also robustly detects tissues of origin of secondary cancers, including metastatic cancers, second primary cancers, and cancers of unknown primaries. Our assessment revealed the ability of this model to characterize pre-cancer samples, a significant step forward in early cancer detection. Deployed broadly this model can deliver accurate diagnosis for a greatly expanded target patient population.

Analysis of multipartite bacterial genomes using alignment free and alignment-based pipelines.

Since the discovery of second chromosome in Rhodobacter sphaeroides 2.4.1 in 1989, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. This has shattered the unipartite (single chromosome) genome dogma in bacteria. Since then, many questions on various aspects of multipartite genomes in bacteria have been addressed. However, our understanding of how multipartite genomes emerge and evolve is still lacking. Importantly, the knowledge of genetic factors underlying the differences in multipartite and single-chromosome genomes is lacking. In this work, we have performed comparative evolutionary and functional genomics analyses to identify molecular factors that discriminate multipartite from unipartite bacteria, with the goal to decipher taxon-specific factors, and those that are prevalent across the taxa, underlying these traits. We assessed the roles of evolutionary mechanisms, specifically gene gain, in driving the divergence of bacteria with single and multiple chromosomes. In addition, we performed functional genomic analysis to garner support for our findings from comparative evolutionary analysis. We found genes such as those encoding conserved hypothetical proteins in Deinococcus radiodurans R1, and putative phage phi-C31 gp36 major capsid like and hypothetical proteins in Rhodobacter sphaeroides 2.4.1, which are located on accessory chromosomes in these bacteria but were not found in the inferred ancestral sequences, and on the primary chromosomes, as well as were not found in their closest relatives with single chromosome within the same clade. Our study shines a new light on the potential roles of the secondary chromosomes in helping bacteria with multipartite genomes to adapt to specialized environments or growth conditions.

Inhaled diesel exhaust particles result in microbiome-related systemic inflammation and altered cardiovascular disease biomarkers in C57Bl/6 male mice.

BACKGROUND: The gut microbiota plays a vital role in host homeostasis and is associated with inflammation and cardiovascular disease (CVD) risk. Exposure to particulate matter (PM) is a known mediator of inflammation and CVD and is reported to promote dysbiosis and decreased intestinal integrity. However, the role of inhaled traffic-generated PM on the gut microbiome and its corresponding systemic effects are not well-characterized. Thus, we investigated the hypothesis that exposure to inhaled diesel exhaust particles (DEP) alters the gut microbiome and promotes microbial-related inflammation and CVD biomarkers. 4-6-week-old male C57Bl/6 mice on either a low-fat (LF, 10% fat) or high-fat (HF, 45% fat) diet were exposed via oropharyngeal aspiration to 35 µg DEP suspended in 35 µl saline or saline only (CON) 2x/week for 30 days. To determine whether probiotics could prevent diet or DEP exposure mediated alterations in the gut microbiome or systemic outcomes, a subset of animals on the HF diet were treated orally with 0.3 g/day (~ 7.5 × 108 CFU/day) of Winclove Ecologic® Barrier probiotics throughout the study. RESULTS: Our results show that inhaled DEP exposure alters gut microbial profiles, including reducing Actinobacteria and expanding Verrucomicrobia and Proteobacteria. We observed increased circulating LPS, altered circulating cytokines (IL-1α, IL-3, IL-13, IL-15, G-CSF, LIF, MIP-2, and TNF-α), and CVD biomarkers (siCAM, PAI-1, sP-Selectin, thrombomodulin, and PECAM) in DEP-exposed and/or HF diet mice. Furthermore, probiotics attenuated the observed reduction of Actinobacteria and expansion of Proteobacteria in DEP-exposed and HF-diet mice. Probiotics mitigated circulating cytokines (IL-3, IL-13, G-CSF, RANTES, and TNF- α) and CVD biomarkers (siCAM, PAI-1, sP-Selectin, thrombomodulin, and PECAM) in respect to DEP-exposure and/or HF diet. CONCLUSION: Key findings of this study are that inhaled DEP exposure alters small intestinal microbial profiles that play a role in systemic inflammation and early CVD biomarkers. Probiotic treatment in this study was fundamental in understanding the role of inhaled DEP on the microbiome and related systemic inflammatory and CVD biomarkers.

Expression of a dominant-negative AtNEET-H89C protein disrupts iron-sulfur metabolism and iron homeostasis in Arabidopsis.

Iron-sulfur (Fe-S) clusters play an essential role in plants as protein cofactors mediating diverse electron transfer reactions. Because they can react with oxygen to form reactive oxygen species (ROS) and inflict cellular damage, the biogenesis of Fe-S clusters is highly regulated. A recently discovered group of 2Fe-2S proteins, termed NEET proteins, was proposed to coordinate Fe-S, Fe and ROS homeostasis in mammalian cells. Here we report that disrupting the function of AtNEET, the sole member of the NEET protein family in Arabidopsis thaliana, triggers leaf-associated Fe-S- and Fe-deficiency responses, elevated Fe content in chloroplasts (1.2-1.5-fold), chlorosis, structural damage to chloroplasts and a high seedling mortality rate. Our findings suggest that disrupting AtNEET function disrupts the transfer of 2Fe-2S clusters from the chloroplastic 2Fe-2S biogenesis pathway to different cytosolic and chloroplastic Fe-S proteins, as well as to the cytosolic Fe-S biogenesis system, and that uncoupling this process triggers leaf-associated Fe-S- and Fe-deficiency responses that result in Fe over-accumulation in chloroplasts and enhanced ROS accumulation. We further show that AtNEET transfers its 2Fe-2S clusters to DRE2, a key protein of the cytosolic Fe-S biogenesis system, and propose that the availability of 2Fe-2S clusters in the chloroplast and cytosol is linked to Fe homeostasis in plants.

Metabolomics technology and bioinformatics for precision medicine.

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Higher-order Markov models for metagenomic sequence classification.

MOTIVATION: Alignment-free, stochastic models derived from k-mer distributions representing reference genome sequences have a rich history in the classification of DNA sequences. In particular, the variants of Markov models have previously been used extensively. Higher-order Markov models have been used with caution, perhaps sparingly, primarily because of the lack of enough training data and computational power. Advances in sequencing technology and computation have enabled exploitation of the predictive power of higher-order models. We, therefore, revisited higher-order Markov models and assessed their performance in classifying metagenomic sequences. RESULTS: Comparative assessment of higher-order models (HOMs, 9th order or higher) with interpolated Markov model, interpolated context model and lower-order models (8th order or lower) was performed on metagenomic datasets constructed using sequenced prokaryotic genomes. Our results show that HOMs outperform other models in classifying metagenomic fragments as short as 100 nt at all taxonomic ranks, and at lower ranks when the fragment size was increased to 250 nt. HOMs were also found to be significantly more accurate than local alignment which is widely relied upon for taxonomic classification of metagenomic sequences. A novel software implementation written in C++ performs classification faster than the existing Markovian metagenomic classifiers and can therefore be used as a standalone classifier or in conjunction with existing taxonomic classifiers for more robust classification of metagenomic sequences. AVAILABILITY AND IMPLEMENTATION: The software has been made available at https://github.com/djburks/SMM. CONTACT: Rajeev.Azad@unt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The impact of multifactorial stress combination on plant growth and survival.

Climate change-driven extreme weather events, combined with increasing temperatures, harsh soil conditions, low water availability and quality, and the introduction of many man-made pollutants, pose a unique challenge to plants. Although our knowledge of the response of plants to each of these individual conditions is vast, we know very little about how a combination of many of these factors, occurring simultaneously, that is multifactorial stress combination, impacts plants. Seedlings of wild-type and different mutants of Arabidopsis thaliana plants were subjected to a multifactorial stress combination of six different stresses, each applied at a low level, and their survival, physiological and molecular responses determined. Our findings reveal that, while each of the different stresses, applied individually, had a negligible effect on plant growth and survival, the accumulated impact of multifactorial stress combination on plants was detrimental. We further show that the response of plants to multifactorial stress combination is unique and that specific pathways and processes play a critical role in the acclimation of plants to multifactorial stress combination. Taken together our findings reveal that further polluting our environment could result in higher complexities of multifactorial stress combinations that in turn could drive a critical decline in plant growth and survival.

MYB30 Orchestrates Systemic Reactive Oxygen Signaling and Plant Acclimation.

Systemic acquired acclimation (SAA) is a key biological process essential for plant survival under conditions of abiotic stress. SAA was recently shown to be controlled by a rapid systemic signaling mechanism termed the reactive oxygen species (ROS) wave in Arabidopsis (Arabidopsis thaliana). MYB30 is a key transcriptional regulator mediating many different biological processes. MYB30 was found to act downstream of the ROS wave in systemic tissues of Arabidopsis in response to local high light (HL) stress treatment. However, the function of MYB30 in systemic signaling and SAA is unknown. To determine the relationship among MYB30, the ROS wave, and systemic acclimation in Arabidopsis, the SAA response to HL stress of myb30 mutants and wild-type plants was determined. Although myb30 plants were found to display enhanced rates of ROS wave propagation and their local tissues acclimated to the HL stress, they were deficient in SAA to HL stress. Compared to wild type, the systemic transcriptomic response of myb30 plants was also deficient, lacking in the expression of over 3,500 transcripts. A putative set of 150 core transcripts directly associated with MYB30 function during HL stress was determined. Our study identifies MYB30 as a key regulator that links systemic ROS signaling with systemic transcriptomic responses, SAA, and plant acclimation to HL stress. In addition, it demonstrates that plant acclimation and systemic ROS signaling are interlinked and that the lack of systemic acclimation drives systemic ROS signaling to occur at faster rates, suggesting a feedback mechanism (potentially involving MYB30) between these two processes.

Seedling Chloroplast Responses Induced by N-Linolenoylethanolamine Require Intact G-Protein Complexes.

In animals, several long-chain N-acylethanolamines (NAEs) have been identified as endocannabinoids and are autocrine signals that operate through cell surface G-protein-coupled cannabinoid receptors. Despite the occurrence of NAEs in land plants, including nonvascular plants, their precise signaling properties and molecular targets are not well defined. Here we show that the activity of N-linolenoylethanolamine (NAE 18:3) requires an intact G-protein complex. Specifically, genetic ablation of the Gßγ dimer or loss of the full set of atypical Gα subunits strongly attenuates an NAE-18:3-induced degreening of cotyledons in Arabidopsis (Arabidopsis thaliana) seedlings. This effect involves, at least in part, transcriptional regulation of chlorophyll biosynthesis and catabolism genes. In addition, there is feedforward transcriptional control of G-protein signaling components and G-protein interactors. These results are consistent with NAE 18:3 being a lipid signaling molecule in plants with a requirement for G-proteins to mediate signal transduction, a situation similar, but not identical, to the action of NAE endocannabinoids in animal systems.

Discovery of mosaic genomic islands in Pseudomonas spp.

Genomic islands, defined as large clusters of genes mobilized through horizontal gene transfer, have a profound impact on evolution of prokaryotes. Recently, we developed a new program, IslandCafe, for identifying such large localized structures in bacterial genomes. A unique attribute of IslandCafe is its ability to decipher mosaic structures within genomic islands. Mosaic genomic islands have generated immense interest due to novel traits that have been attributed to such islands. To provide the Pseudomonas research community a catalogue of mosaic islands in Pseudomonas spp., we applied IslandCafe to decipher genomic islands in 224 completely sequenced genomes of Pseudomonas spp. We also performed comparative genomic analysis using BLAST to infer potential sources of distinct segments within genomic islands. Of the total 4271 genomic islands identified in Pseudomonas spp., 1036 were found to be mosaic. We also identified drug-resistant and pathogenic genomic islands and their potential donors. Our analysis provides a useful resource for Pseudomonas research community to further examine and interrogate mosaic islands in the genomes of interest and understand their role in the emergence and evolution of novel traits.

The impact of water deficit and heat stress combination on the molecular response, physiology, and seed production of soybean.

A combination of drought and heat stress, occurring at the vegetative or reproductive growth phase of many different crops can have a devastating impact on yield. In soybean (Glycine max), a considerable effort has been made to develop genotypes with enhanced yield production under conditions of drought or heat stress. However, how these genotypes perform in terms of growth, physiological responses, and most importantly seed production, under conditions of drought and heat combination is mostly unknown. Here, we studied the impact of water deficit and heat stress combination on the physiology, seed production, and yield per plant of two soybean genotypes, Magellan and Plant Introduction (PI) 548313, that differ in their reproductive responses to heat stress. Our findings reveal that although PI 548313 produced more seeds than Magellan under conditions of heat stress, under conditions of water deficit, and heat stress combination its seed production decreased. Because the number of flowers and pollen germination of PI 548313 remained high under heat or water deficit and heat combination, the reduced seed production exhibited by PI 548313 under the stress combination could be a result of processes that occur at the stigma, ovaries and/or other parts of the flower following pollen germination.

Traffic generated emissions alter the lung microbiota by promoting the expansion of Proteobacteria in C57Bl/6 mice placed on a high-fat diet.

Air pollution has been documented to contribute to severe respiratory diseases like asthma and chronic obstructive pulmonary disorder (COPD). Although these diseases demonstrate a shift in the lung microbiota towards Proteobacteria, the effects of traffic generated emissions on lung microbiota profiles have not been well-characterized. Thus, we investigated the hypothesis that exposure to traffic-generated emissions can alter lung microbiota and immune defenses. Since a large population of the Western world consumes a diet rich in fats, we sought to investigate the synergistic effects of mixed vehicle emissions and high-fat diet consumption. We exposed 3-month-old male C57Bl/6 mice placed either on regular chow (LF) or a high-fat (HF: 45% kcal fat) diet to mixed emissions (ME: 30 µg PM/m3 gasoline engine emissions+70 µg PM/m3 diesel engine emissions) or filtered air (FA) for 6 h/d, 7 d/wk for 30 days. Levels of pulmonary immunoglobulins IgA, IgG, and IgM were analyzed by ELISA, and lung microbial profiling was done using qPCR and Illumina 16 S sequencing. We observed a significant decrease in lung IgA in the ME-exposed animals, compared to the FA-exposed animals, both fed a HF diet. Our results also revealed a significant decrease in lung IgG in the ME-exposed animals both on the LF diet and HF diet, in comparison to the FA-exposed animals. We also observed an expansion of Enterobacteriaceae belonging to the Proteobacteria phylum in the ME-exposed groups on the HF diet. Collectively, we show that the combined effects of ME and HF diet result in decreased immune surveillance and lung bacterial dysbiosis, which is of significance in lung diseases.

Identification and characterization of a core set of ROS wave-associated transcripts involved in the systemic acquired acclimation response of Arabidopsis to excess light.

Systemic acquired acclimation (SAA) plays a key role in optimizing growth and preventing damage associated with fluctuating or abrupt changes in the plant environment. To be effective, SAA has to occur at a rapid rate and depend on rapid signaling pathways that transmit signals from affected tissues to all parts of the plant. Although recent studies have identified several different rapid systemic signaling pathways that could mediate SAA, very little information is known about the extent of their involvement in mediating transcriptomic responses. Here we reveal that the systemic transcriptomic response of plants to excess light stress is extensive in its context and involves an early (2 min) and transient stage of transcript expression that includes thousands of genes. This early response is dependent on the respiratory burst oxidase homolog D protein, and the function of the reactive oxygen species (ROS) wave. We further identify a core set of transcripts associated with the ROS wave and suggest that some of these transcripts are involved in linking ROS with calcium signaling. Priming of a systemic leaf to become acclimated to a particular stress during SAA involves thousands of transcripts that display a rapid and transient expression pattern driven by the ROS wave.

Prions: Roles in Development and Adaptive Evolution.

Prions are often considered as anomalous proteins associated primarily with disease rather than as a fundamental source of diversity within biological proteomes. Whereas this longstanding viewpoint has its genesis in the discovery of the original namesake prions as causative agents of several complex diseases, the underlying assumption of a strict disease basis for prions could not be further from the truth. Prions and the spectrum of functions they comprise, likely represent one of the largest paradigm shifts concerning molecular-encoded phenotypic diversity since identification of DNA as the principle molecule of heredity. The ability of prions to recruit similar proteins to alternate conformations may engender a reservoir of diversity supplementing the genetic diversity resulting from stochastic mutations of DNA and subsequent natural selection. Here we present several currently known prions and how many of their functions as well as modes of transmission are intricately linked to adaptation from an evolutionary perspective. Further, the stability of some prion conformations across generations indicates that heritable prion-based adaptation is a reality.