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The circadian system coordinates daily rhythms in lipid metabolism, storage and utilization. Disruptions of internal circadian rhythms due to altered sleep/wake schedules, such as in night-shift work, have been implicated in increased risk of cardiovascular disease and metabolic disorders. To determine the impact of a night-shift schedule on the human blood plasma lipidome, an in-laboratory simulated shift work study was conducted.Fourteen healthy young adults were assigned to 3 days of either a simulated day or night-shift schedule, followed by a 24-h constant routine protocol with fixed environmental conditions, hourly isocaloric snacks, and constant wakefulness to investigate endogenous circadian rhythms. Blood plasma samples collected at 3-h intervals were subjected to untargeted lipidomics analysis.More than 400 lipids were identified and quantified across 21 subclasses. Focusing on lipids with low between-subject variation per shift condition, alterations in the circulating plasma lipidome revealed generally increased mean triglyceride levels and decreased mean phospholipid levels after night-shift relative to day-shift. The circadian rhythms of triglycerides containing odd chain fatty acids peaked earlier during constant routine after night-shift. Regardless of shift condition, triglycerides tended to either peak or be depleted at 16:30 h, with chain-specific differences associated with the direction of change.The simulated night-shift schedule was associated with altered temporal patterns in the lipidome. This may be premorbid to the elevated cardiovascular risk that has been found epidemiologically in night-shift workers.

Background Microbiomes contribute to multiple ecosystem services by transforming organic matter in the soil. Extreme shifts in the environment, such as drying-rewetting cycles during drought, can impact the microbial metabolism of organic matter by altering microbial physiology and function. These physiological responses are mediated in part by lipids that are responsible for regulating interactions between cells and the environment. Despite this critical role in regulating the microbial response to stress, little is known about microbial lipids and metabolites in the soil or how they influence phenotypes that are expressed under drying-rewetting cycles. To address this knowledge gap, we conducted a soil incubation experiment to simulate soil drying during a summer drought of an arid grassland, then measured the response of the soil lipidome and metabolome during the first 3 h after wet-up. Results Reduced nutrient access during soil drying incurred a replacement of membrane phospholipids, resulting in a diminished abundance of multiple phosphorus-rich membrane lipids. The hot and dry conditions increased the prevalence of sphingolipids and lipids containing long-chain polyunsaturated fatty acids, both of which are associated with heat and osmotic stress-mitigating properties in fungi. This novel finding suggests that lipids commonly present in eukaryotes such as fungi may play a significant role in supporting community resilience displayed by arid land soil microbiomes during drought. As early as 10 min after rewetting dry soil, distinct changes were observed in several lipids that had bacterial signatures including a rapid increase in the abundance of glycerophospholipids with saturated and short fatty acid chains, prototypical of bacterial membrane lipids. Polar metabolites including disaccharides, nucleic acids, organic acids, inositols, and amino acids also increased in abundance upon rewetting. This rapid metabolic reactivation and growth after rewetting coincided with an increase in the relative abundance of firmicutes, suggesting that members of this phylum were positively impacted by rewetting. Conclusions Our study revealed specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. The drought-induced nutrient limitation was reflected in the lipidome and polar metabolome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long-chain polyunsaturated fatty acids in dry soil suggest that lipids likely play an important role in the drought tolerance of microbial taxa capable of synthesizing these lipids. An increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest a metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions.

SummaryThe detailed mechanisms of COVID-19 infection pathology remain poorly understood. To improve our understanding of SARS-CoV-2 pathology, we performed a multi-omics analysis of an immunologically naïve SARS-CoV-2 clinical cohort from the plasma of uninfected controls, mild, and severe infections. A comparison of healthy controls and patient samples showed activation of neutrophil degranulation pathways and formation of neutrophil extracellular trap (NET) complexes that were activated in a subset of the mild infections and more prevalent in severe infections (containing multiple NET proteins in individual patient samples). As a potential mechanism to suppress NET formation, multiple redox enzymes were elevated in the mild and severe symptom population. Analysis of metabolites from the same cohort showed a 24- and 60-fold elevation in plasma L-cystine, the oxidized form of cysteine, which is a substrate of the powerful antioxidant glutathione, in mild and severe patients, respectively. Unique to patients with mild infections, the carnosine dipeptidase modifying enzyme (CNDP1) was up-regulated. The strong protein and metabolite oxidation signatures suggest multiple compensatory pathways working to suppress oxidation and NET formation in SARS-CoV-2 infections.

Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we perform comprehensive lipidomics to create a lipid network map during ZIKV infection. We find that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein results in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocks ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites, and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection., Zika virus (ZIKV) remodels intracellular membranes for replication, but the role of different lipid types for infection and disease is unclear. Here, the authors perform lipidomics, show perturbation of the lipid network during ZIKV infection and show that ceramides are important for ZIKV infection.

Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants. Our analysis revealed two pathogenicity-related gene expression clusters; these results were corroborated by matching proteomics data. We also identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks (nodes that bridge different network regions) were highly enriched in pathogenicity-related genes, while network hubs (highly connected network nodes) were significantly depleted in these genes. We confirmed that this trend persisted in a distinct virus: Severe Acute Respiratory Syndrome Coronavirus (SARS). The role of epidermal growth factor receptor (EGFR) in influenza pathogenesis, one of the bottleneck regulators with corroborating signals across transcript and protein expression data, was tested and validated in additional mouse infection experiments. We demonstrate that EGFR is important during influenza infection, but the role it plays changes for lethal versus non-lethal infections. Our results show that by using association networks, bottleneck genes that lack hub characteristics can be used to predict a gene's involvement in influenza virus pathogenicity. We also demonstrate the utility of employing multiple network approaches for analyzing host response data from viral infections.

Significance Both highly pathogenic avian influenza virus and Middle East respiratory syndrome coronavirus (MERS-CoV) infections are characterized by severe disease and high mortality. The continued threat of their emergence from zoonotic populations underscores an important need to understand the dynamics of their infection. By comparing the host responses across other related respiratory virus infections, these studies have identified a common avenue used by MERS-CoV and A/influenza/Vietnam/1203/2004 (H5N1-VN1203) influenza to antagonize antigen presentation through epigenetic modulation. Overall, the use of cross-comparisons provides an additional approach to leverage systems biology data to identify key pathways and strategies used by viruses to subvert host immune responses and may be critical in developing both vaccines and therapeutic treatment., Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ–dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV–mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.

Coronavirus (CoV) emergence in both humans and livestock represents a significant threat to global public health, as evidenced by the sudden emergence of severe acute respiratory syndrome CoV (SARS-CoV), MERS-CoV, porcine epidemic diarrhea virus, and swine delta CoV in the 21st century. These studies describe an approach that effectively targets the highly conserved 2′O-MTase activity of CoVs for attenuation. With clear understanding of the IFN/IFIT (IFN-induced proteins with tetratricopeptide repeats)-based mechanism, NSP16 mutants provide a suitable target for a live attenuated vaccine platform, as well as therapeutic development for both current and future emergent CoV strains. Importantly, other approaches targeting other conserved pan-CoV functions have not yet proven effective against MERS-CoV, illustrating the broad applicability of targeting viral 2′O-MTase function across CoVs., Coronaviruses (CoVs) encode a mixture of highly conserved and novel genes, as well as genetic elements necessary for infection and pathogenesis, raising the possibility of common targets for attenuation and therapeutic design. In this study, we focused on highly conserved nonstructural protein 16 (NSP16), a viral 2′O-methyltransferase (2′O-MTase) that encodes critical functions in immune modulation and infection. Using reverse genetics, we disrupted a key motif in the conserved KDKE motif of Middle East respiratory syndrome CoV (MERS-CoV) NSP16 (D130A) and evaluated the effect on viral infection and pathogenesis. While the absence of 2′O-MTase activity had only a marginal impact on propagation and replication in Vero cells, dNSP16 mutant MERS-CoV demonstrated significant attenuation relative to the control both in primary human airway cell cultures and in vivo. Further examination indicated that dNSP16 mutant MERS-CoV had a type I interferon (IFN)-based attenuation and was partially restored in the absence of molecules of IFN-induced proteins with tetratricopeptide repeats. Importantly, the robust attenuation permitted the use of dNSP16 mutant MERS-CoV as a live attenuated vaccine platform protecting from a challenge with a mouse-adapted MERS-CoV strain. These studies demonstrate the importance of the conserved 2′O-MTase activity for CoV pathogenesis and highlight NSP16 as a conserved universal target for rapid live attenuated vaccine design in an expanding CoV outbreak setting. IMPORTANCE Coronavirus (CoV) emergence in both humans and livestock represents a significant threat to global public health, as evidenced by the sudden emergence of severe acute respiratory syndrome CoV (SARS-CoV), MERS-CoV, porcine epidemic diarrhea virus, and swine delta CoV in the 21st century. These studies describe an approach that effectively targets the highly conserved 2′O-MTase activity of CoVs for attenuation. With clear understanding of the IFN/IFIT (IFN-induced proteins with tetratricopeptide repeats)-based mechanism, NSP16 mutants provide a suitable target for a live attenuated vaccine platform, as well as therapeutic development for both current and future emergent CoV strains. Importantly, other approaches targeting other conserved pan-CoV functions have not yet proven effective against MERS-CoV, illustrating the broad applicability of targeting viral 2′O-MTase function across CoVs.

Coronaviruses encode a mix of highly conserved and novel genes as well as genetic elements necessary for infection and pathogenesis, raising the possibility for common targets for attenuation and therapeutic design. In this study, we focus on the highly conserved nonstructural protein (NSP) 16, a viral 2’O methyl-transferase (MTase) that encodes critical functions in immune modulation and infection. Using reverse genetics, we disrupted a key motif in the conserved KDKE motif of MERS NSP16 (D130A) and evaluated the effect on viral infection and pathogenesis. While the absence of 2’O MTase activity had only marginal impact on propagation and replication in Vero cells, the MERS dNSP16 mutant demonstrated significant attenuation relative to control both in primary human airway cultures andin vivo.Further examination indicated the MERS dNSP16 mutant had a type I IFN based attenuation and was partially restored in the absence of IFIT molecules. Importantly, the robust attenuation permitted use of MERS dNSP16 as a live attenuated vaccine platform protecting from challenge with a mouse adapted MERS-CoV strain. These studies demonstrate the importance of the conserved 2’O MTase activity for CoV pathogenesis and highlight NSP16 as a conserved universal target for rapid live attenuated vaccine design in an expanding CoV outbreak setting.SignificanceCoronavirus emergence in both human and livestock represents a significant threat to global public health, as evidenced by the sudden emergence of SARS-CoV, MERS-CoV, PEDV and swine delta coronavirus in the 21stcentury. These studies describe an approach that effectively targets the highly conserved 2’O methyl-transferase activity of coronaviruses for attenuation. With clear understanding of the IFN/IFIT based mechanism, NSP16 mutants provide a suitable target for a live attenuated vaccine platform as well as therapeutic development for both current and future emergent CoV strains. Importantly, other approaches targeting other conserved pan-coronavirus functions have not yet proven effective against MERS-CoV, illustrating the broad applicability of targeting viral 2’O MTase function across coronaviruses.

While dispensable for viral replication, coronavirus (CoV) accessory open reading frame (ORF) proteins often play critical roles during infection and pathogenesis. Utilizing a previously generated mutant, we demonstrate that the absence of all four Middle East respiratory syndrome CoV (MERS-CoV) accessory ORFs (deletion of ORF3, -4a, -4b, and -5 [dORF3-5]) has major implications for viral replication and pathogenesis. Importantly, attenuation of the dORF3-5 mutant is primarily driven by dysregulated host responses, including disrupted cell processes, augmented interferon (IFN) pathway activation, and robust inflammation. In vitro replication attenuation also extends to in vivo models, allowing use of dORF3-5 as a live attenuated vaccine platform. Finally, examination of ORF5 implicates a partial role in modulation of NF-κB-mediated inflammation. Together, the results demonstrate the importance of MERS-CoV accessory ORFs for pathogenesis and highlight them as potential targets for surveillance and therapeutic treatments moving forward., IMPORTANCE The initial emergence and periodic outbreaks of MERS-CoV highlight a continuing threat posed by zoonotic pathogens to global public health. In these studies, mutant virus generation demonstrates the necessity of accessory ORFs in regard to MERS-CoV infection and pathogenesis. With this in mind, accessory ORF functions can be targeted for both therapeutic and vaccine treatments in response to MERS-CoV and related group 2C coronaviruses. In addition, disruption of accessory ORFs in parallel may offer a rapid response platform to attenuation of future emergent strains based on both SARS- and MERS-CoV accessory ORF mutants.

In the U.S., 6% of the general population suffers from a rare disorder that has evaded diagnosis, defined by U.S. law as one that affects Drosophila and zebrafish. These data are being compared against similar metabolic profiles from healthy individuals and in integrative analyses together with results from patient gene sequencing. Study design is a major challenge in omics analyses of undiagnosed diseases, which may affect only one or a few patients. To allow for proper statistical analyses of UDN patient data, we generated reference datasets from analyses of plasma, CSF, and urine from >391 individuals with no known metabolic disease and representative of the demographics of the UDN (predominately < 18 yrs, ~50% female, and majority Caucasian), all driven by power analyses of historical data from our previous work. Metabolomics analyses were performed using GC-MS, with metabolite identification via an in-house modified version of FiehnLib. Lipidomics analyses were performed using LC-MS/MS, with lipid identification via the in-house tool LIQUID. Metabolomics data were integrated with gene sequencing data using STITCH. To date, we have performed >1500 untargeted metabolomics and lipidomics analyses of plasma, CSF, and urine samples from individuals with no known metabolic disease in order to create reference databases against which data from UDN patients can be compared. These analyses have resulted in reference datasets containing >300 identified and unidentified metabolites and >500 identified lipids as a community resource. Using data from metabolomics analyses of 139 quality control (QC) samples distributed among 201 samples (340 total analyses), the median coefficient of variation (CV) in the measurement of 180 metabolites was 27%. Similarly, using data from lipidomics analyses of 100 QC samples distributed among 197 samples (297 total analyses), the median CV in the measurement of 361 lipids was 10%. A total of 145 samples (83 plasma, 57 urine, and 5 CSF) from 83 UDN patients and first degree relatives have been analyzed, and the resulting data compared to the appropriate reference datasets in order to identify outlier metabolites and lipids. This data has subsequently been used to identify the metabolic pathways that have been affected by the underlying disease processes. In a proof-of-principle analysis, metabolomics data and gene sequencing results from 18 UDN cases were integrated using STITCH (Search Tool for Interactions of Chemicals; stitch.embl.de). From a list of several candidate gene variants, STITCH was able to identify a single gene/protein as a key interactor with metabolic outliers and their partners, based on a protein-chemical database and random decision forests (e.g. p-values for protein-protein interactions). The biological relevance of genes of clinical interest will be subsequently evaluated by functional assays.

The use of dried blood spots (DBS) has many advantages over traditional plasma and serum samples such as the smaller blood volume required, storage at room temperature, and ability to sample in remote locations. However, understanding the robustness of different analytes in DBS samples is essential, especially in older samples collected for longitudinal studies.Here we analyzed the stability of polar metabolites and lipids in DBS samples collected in 2000-2001 and stored at room temperature. The identified and statistically significant molecules were then compared to matched serum samples stored at -80°C to determine if the DBS samples could be effectively used in a longitudinal study following metabolic disease.A total of 400 polar metabolites and lipids were identified in the serum and DBS samples using gas chromatograph/mass spectrometry (GC/MS), liquid chromatography (LC)/MS, and LC/ion mobility spectrometry-MS (LC/IMS-MS). The identified polar metabolites overlapped well between the sample types, though only one statistically significant metabolite was conserved in a case-control study of older diabetic males with low amounts of high-density lipoproteins and high body mass indices, triacylglycerides and glucose levels when compared to non-diabetic patients with normal levels, indicating that degradation in the DBS samples affects polar metabolite quantitation. Differences in the lipid identifications indicated that some oxidation occurs in the DBS samples. However, 36 statistically significant lipids correlated in both sample types.The difference in the number of statistically significant polar metabolites and lipids indicated that the lipids did not degrade to as great of a degree as the polar metabolites in the DBS samples and lipid quantitation was still possible. Copyright © 2016 John WileySons, Ltd.

Summary The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform 'omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. This work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.

Sequential methods are well established for randomized clinical trials (RCTs), and their use in observational settings has increased with the development of national vaccine and drug safety surveillance systems that monitor large healthcare databases. Observational safety monitoring requires that sequential testing methods be better equipped to incorporate confounder adjustment and accommodate rare adverse events. New methods designed specifically for observational surveillance include a group sequential likelihood ratio test that uses exposure matching and generalized estimating equations approach that involves regression adjustment. However, little is known about the statistical performance of these methods or how they compare to RCT methods in both observational and rare outcome settings. We conducted a simulation study to determine the type I error, power and time-to-surveillance-end of group sequential likelihood ratio test, generalized estimating equations and RCT methods that construct group sequential Lan-DeMets boundaries using data from a matched (group sequential Lan-DeMets-matching) or unmatched regression (group sequential Lan-DeMets-regression) setting. We also compared the methods using data from a multisite vaccine safety study. All methods had acceptable type I error, but regression methods were more powerful, faster at detecting true safety signals and less prone to implementation difficulties with rare events than exposure matching methods. Method performance also depended on the distribution of information and extent of confounding by site. Our results suggest that choice of sequential method, especially the confounder control strategy, is critical in rare event observational settings. These findings provide guidance for choosing methods in this context and, in particular, suggest caution when conducting exposure matching.

Although the presence of nodal disease is prognostic in thymic malignancy, the significance of the extent of nodal disease has yet to be defined. Lymph node dissection has not been routinely performed, and there is currently no node map defined for thymic malignancy. To establish a universal language for reporting as well as characterize the staging of this disease more accurately, an empiric node map is proposed here. This was developed using prior classification systems, series reporting specifics of nodal involvement, anatomical studies of lymphatic drainage, and preexisting node maps of the chest as defined by the International Association for the Study of Lung Cancer and the neck as defined by the American Academy of OtolaryngologyHead and Neck Surgery and the American Society for Head and Neck Surgery. The development of this node map was a joint effort by the International Thymic Malignancy Interest Group and the Thymic Domain of the IASLC Staging and Prognostic Factors Committee. It was reviewed and subsequently approved by the members of ITMIG. This map will be used as an adjunct to define node staging as part of a universal stage classification for thymic malignancy. As more data are gathered using definitions set forth by this node map, a revision may be undertaken in the future.