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Objective To systematically review studies quantifying the associations of long term (clinic), mid-term (home), and short term (ambulatory) variability in blood pressure, independent of mean blood pressure, with cardiovascular disease events and mortality.Data sources Medline, Embase, Cinahl, and Web of Science, searched to 15 February 2016 for full text articles in English.Eligibility criteria for study selection Prospective cohort studies or clinical trials in adults, except those in patients receiving haemodialysis, where the condition may directly impact blood pressure variability. Standardised hazard ratios were extracted and, if there was little risk of confounding, combined using random effects meta-analysis in main analyses. Outcomes included all cause and cardiovascular disease mortality and cardiovascular disease events. Measures of variability included standard deviation, coefficient of variation, variation independent of mean, and average real variability, but not night dipping or day-night variation.Results 41 papers representing 19 observational cohort studies and 17 clinical trial cohorts, comprising 46 separate analyses were identified. Long term variability in blood pressure was studied in 24 papers, mid-term in four, and short-term in 15 (two studied both long term and short term variability). Results from 23 analyses were excluded from main analyses owing to high risks of confounding. Increased long term variability in systolic blood pressure was associated with risk of all cause mortality (hazard ratio 1.15, 95% confidence interval 1.09 to 1.22), cardiovascular disease mortality (1.18, 1.09 to 1.28), cardiovascular disease events (1.18, 1.07 to 1.30), coronary heart disease (1.10, 1.04 to 1.16), and stroke (1.15, 1.04 to 1.27). Increased mid-term and short term variability in daytime systolic blood pressure were also associated with all cause mortality (1.15, 1.06 to 1.26 and 1.10, 1.04 to 1.16, respectively).Conclusions Long term variability in blood pressure is associated with cardiovascular and mortality outcomes, over and above the effect of mean blood pressure. Associations are similar in magnitude to those of cholesterol measures with cardiovascular disease. Limited data for mid-term and short term variability showed similar associations. Future work should focus on the clinical implications of assessment of variability in blood pressure and avoid the common confounding pitfalls observed to date.Systematic review registration PROSPERO CRD42014015695.

Multiple models describe the formation and evolution of distinct microbial phylogenetic groups. These evolutionary models make different predictions regarding how adaptive alleles spread through populations and how genetic diversity is maintained. Processes predicted by competing evolutionary models, for example, genome-wide selective sweeps vs gene-specific sweeps, could be captured in natural populations using time-series metagenomics if the approach were applied over a sufficiently long time frame. Direct observations of either process would help resolve how distinct microbial groups evolve. Here, from a 9-year metagenomic study of a freshwater lake (2005–2013), we explore changes in single-nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in 30 bacterial populations. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied by >1000-fold among populations. SNP allele frequencies also changed dramatically over time within some populations. Interestingly, nearly all SNP variants were slowly purged over several years from one population of green sulfur bacteria, while at the same time multiple genes either swept through or were lost from this population. These patterns were consistent with a genome-wide selective sweep in progress, a process predicted by the ‘ecotype model’ of speciation but not previously observed in nature. In contrast, other populations contained large, SNP-free genomic regions that appear to have swept independently through the populations prior to the study without purging diversity elsewhere in the genome. Evidence for both genome-wide and gene-specific sweeps suggests that different models of bacterial speciation may apply to different populations coexisting in the same environment.

Although microbes mediate much of the biogeochemical cycling in freshwater, the categories of carbon and nutrients currently used in models of freshwater biogeochemical cycling are too broad to be relevant on a microbial scale. One way to improve these models is to incorporate microbial data. Here, we analyze both genes and genomes from three metagenomic time series and propose specific roles for microbial taxa in freshwater biogeochemical cycles. Our metagenomic time series span multiple years and originate from a eutrophic lake (Lake Mendota) and a humic lake (Trout Bog Lake) with contrasting water chemistry. Our analysis highlights the role of polyamines in the nitrogen cycle, the diversity of diazotrophs between lake types, the balance of assimilatory vs. dissimilatory sulfate reduction in freshwater, the various associations between types of phototrophy and carbon fixation, and the density and diversity of glycoside hydrolases in freshwater microbes. We also investigated aspects of central metabolism such as hydrogen metabolism, oxidative phosphorylation, methylotrophy, and sugar degradation. Finally, by analyzing the dynamics over time in nitrogen fixation genes and Cyanobacteria genomes, we show that the potential for nitrogen fixation is linked to specific populations in Lake Mendota. This work represents an important step towards incorporating microbial data into ecosystem models and provides a better understanding of how microbes may participate in freshwater biogeochemical cycling.

In this paper, we describe why and how to build a local community of practice in scientific programming for life scientists who use computers and programming in their research. A community of practice is a small group of scientists who meet regularly to help each other and promote good practices in scientific programming. While most life scientists are well trained in the laboratory to conduct experiments, good practices with (big) data sets and their analysis are often missing. We propose a model on how to build such a community of practice at a local academic institution, present two real-life examples, and introduce challenges and implemented solutions. We believe that the current data deluge that life scientists face can benefit from the implementation of these small communities. Good practices spread among experimental scientists will foster open, transparent, and sound scientific results beneficial to society. Good practices in scientific programming are needed to increase research reproducibility Modern biology is facing reproducibility issues [12]. While evidence suggests that this might not be as bad as it sounds [13], there is clearly a need for increased reproducibility. For

Accurately assessing the production of bioaccumulative neurotoxic methylmercury by characterizing the phylogenetic diversity, metabolic functions, and activity of methylators in the environment is crucial for understanding constraints on the mercury cycle. Much of our understanding of methylmercury production is based on cultured anaerobic microorganisms within the Deltaproteobacteria , Firmicutes , and Euryarchaeota. Advances in next-generation sequencing technologies have enabled large-scale cultivation-independent surveys of diverse and poorly characterized microorganisms from numerous ecosystems. We used genome-resolved metagenomics and metatranscriptomics to highlight the vast phylogenetic and metabolic diversity of putative mercury methylators and their depth-discrete activities in thawing permafrost. This work underscores the importance of using genome-resolved metagenomics to survey specific putative methylating populations of a given mercury-impacted ecosystem. Methylmercury is a potent bioaccumulating neurotoxin that is produced by specific microorganisms that methylate inorganic mercury. Methylmercury production in diverse anaerobic bacteria and archaea was recently linked to the hgcAB genes. However, the full phylogenetic and metabolic diversity of mercury-methylating microorganisms has not been fully unraveled due to the limited number of cultured experimentally verified methylators and the limitations of primer-based molecular methods. Here, we describe the phylogenetic diversity and metabolic flexibility of putative mercury-methylating microorganisms by hgcAB identification in publicly available isolate genomes and metagenome-assembled genomes (MAGs) as well as novel freshwater MAGs. We demonstrate that putative mercury methylators are much more phylogenetically diverse than previously known and that hgcAB distribution among genomes is most likely due to several independent horizontal gene transfer events. The microorganisms we identified possess diverse metabolic capabilities spanning carbon fixation, sulfate reduction, nitrogen fixation, and metal resistance pathways. We identified 111 putative mercury methylators in a set of previously published permafrost metatranscriptomes and demonstrated that different methylating taxa may contribute to hgcA expression at different depths. Overall, we provide a framework for illuminating the microbial basis of mercury methylation using genome-resolved metagenomics and metatranscriptomics to identify putative methylators based upon hgcAB presence and describe their putative functions in the environment. IMPORTANCE Accurately assessing the production of bioaccumulative neurotoxic methylmercury by characterizing the phylogenetic diversity, metabolic functions, and activity of methylators in the environment is crucial for understanding constraints on the mercury cycle. Much of our understanding of methylmercury production is based on cultured anaerobic microorganisms within the Deltaproteobacteria , Firmicutes , and Euryarchaeota. Advances in next-generation sequencing technologies have enabled large-scale cultivation-independent surveys of diverse and poorly characterized microorganisms from numerous ecosystems. We used genome-resolved metagenomics and metatranscriptomics to highlight the vast phylogenetic and metabolic diversity of putative mercury methylators and their depth-discrete activities in thawing permafrost. This work underscores the importance of using genome-resolved metagenomics to survey specific putative methylating populations of a given mercury-impacted ecosystem.

Diabetic cardiomyopathy (DCM) is defined as cardiac disease independent of vascular complications during diabetes. The number of new cases of DCM is rising at epidemic rates in proportion to newly diagnosed cases of diabetes mellitus (DM) throughout the world. DCM is a heart failure syndrome found in diabetic patients that is characterized by left ventricular hypertrophy and reduced diastolic function, with or without concurrent systolic dysfunction, occurring in the absence of hypertension and coronary artery disease. DCM and other diabetic complications are caused in part by elevations in blood glucose and lipids, characteristic of DM. Although there are pathological consequences to hyperglycemia and hyperlipidemia, the combination of the two metabolic abnormalities potentiates the severity of diabetic complications. A natural competition exists between glucose and fatty acid metabolism in the heart that is regulated by allosteric and feedback control and transcriptional modulation of key limiting enzymes. Inhibition of these glycolytic enzymes not only controls flux of substrate through the glycolytic pathway, but also leads to the diversion of glycolytic intermediate substrate through pathological pathways, which mediate the onset of diabetic complications. The present review describes the limiting steps involved in the development of these pathological pathways and the factors involved in the regulation of these limiting steps. Additionally, therapeutic options with demonstrated or postulated effects on DCM are described.

Influenza virus, respiratory syncytial virus, human metapneumovirus, parainfluenza virus, coronaviruses, and rhinoviruses are among the most common viruses causing mild seasonal colds. These RNA viruses can also cause lower respiratory tract infections leading to bronchiolitis and pneumonia. Young children, the elderly, and patients with compromised cardiac, pulmonary, or immune systems are at greatest risk for serious disease associated with these RNA virus respiratory infections. In addition, swine and avian influenza viruses, together with severe acute respiratory syndrome-associated and Middle Eastern respiratory syndrome coronaviruses, represent significant pandemic threats to the general population. In this review, we describe the current medical need resulting from respiratory infections caused by RNA viruses, which justifies drug discovery efforts to identify new therapeutic agents. The RNA polymerase of respiratory viruses represents an attractive target for nucleoside and nucleotide analogs acting as inhibitors of RNA chain synthesis. Here, we present the molecular, biochemical, and structural fundamentals of the polymerase of the four major families of RNA respiratory viruses: Orthomyxoviridae, Pneumoviridae/Paramyxoviridae, Coronaviridae, and Picornaviridae. We summarize past and current efforts to develop nucleoside and nucleotide analogs as antiviral agents against respiratory virus infections. This includes molecules with very broad antiviral spectrum such as ribavirin and T-705 (favipiravir), and others targeting more specifically one or a few virus families. Recent advances in our understanding of the structure(s) and function(s) of respiratory virus polymerases will likely support the discovery and development of novel nucleoside analogs.

Big data abound in microbiology, but the workflows designed to enable researchers to interpret data can constrain the biological questions that can be asked. Five years after anvi’o was first published, this community-led multi-omics platform is maturing into an open software ecosystem that reduces constraints in ‘omics data analyses.

Thyroid hormones are important modulators of metabolic activity in mammals and alter cholesterol and fatty acid levels through activation of the nuclear thyroid hormone receptor (THR). Currently, there are several THRβ agonists in clinical trials for the treatment of non-alcoholic steatohepatitis (NASH) that have demonstrated the potential to reduce liver fat and restore liver function. In this study, we tested three THRβ-agonism-based NASH treatment candidates, GC-1 (sobetirome), MGL-3196 (resmetirom), and VK2809, and compared their selectivity for THRβ and their ability to modulate the expression of genes specific to cholesterol and fatty acid biosynthesis and metabolism in vitro using human hepatic cells and in vivo using a rat model. Treatment with GC-1 upregulated the transcription of CPT1A in the human hepatocyte-derived Huh-7 cell line with a dose-response comparable to that of the native THR ligand, triiodothyronine (T3). VK2809A (active parent of VK2809), MGL-3196, and VK2809 were approximately 30-fold, 1,000-fold, and 2,000-fold less potent than T3, respectively. Additionally, these relative potencies were confirmed by quantification of other direct gene targets of THR, namely, ANGPTL4 and DIO1 . In primary human hepatocytes, potencies were conserved for every compound except for VK2809, which showed significantly increased potency that was comparable to that of its active counterpart, VK2809A. In high-fat diet fed rats, a single dose of T3 significantly reduced total cholesterol levels and concurrently increased liver Dio1 and Me1 RNA expression. MGL-3196 treatment resulted in concentration-dependent decreases in total and low-density lipoprotein cholesterol with corresponding increases in liver gene expression, but the compound was significantly less potent than T3. In conclusion, we have implemented a strategy to rank the efficacy of THRβ agonists by quantifying changes in the transcription of genes that lead to metabolic alterations, an effect that is directly downstream of THR binding and activation.