The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole

Eukaryotic phytoplankton are responsible for at least 20% of annual global carbon fixation. Their diversity and activity are shaped by interactions with prokaryotes as part of complex microbiomes. Although differences in their local species diversity have been estimated, we still have a limited understanding of environmental conditions responsible for compositional differences between local species communities on a large scale from pole to pole. Here, we show, based on pole-to-pole phytoplankton metatranscriptomes and microbial rDNA sequencing, that environmental differences between polar and non-polar upper oceans most strongly impact the large-scale spatial pattern of biodiversity and gene activity in algal microbiomes. The geographic differentiation of co-occurring microbes in algal microbiomes can be well explained by the latitudinal temperature gradient and associated break points in their beta diversity, with an average breakpoint at 14 °C ± 4.3, separating cold and warm upper oceans. As global warming impacts upper ocean temperatures, we project that break points of beta diversity move markedly pole-wards. Hence, abrupt regime shifts in algal microbiomes could be caused by anthropogenic climate change

Gut Pharmacomicrobiomics: the tip of an iceberg of complex interactions between drugs and gut-associated microbes

Abstract The influence of resident gut microbes on xenobiotic metabolism has been investigated at different levels throughout the past five decades. However, with the advance in sequencing and pyrotagging technologies, addressing the influence of microbes on xenobiotics had to evolve from assessing direct metabolic effects on toxins and botanicals by conventional culture-based techniques to elucidating the role of community composition on drugs metabolic profiles through DNA sequence-based phylogeny and metagenomics. Following the completion of the Human Genome Project, the rapid, substantial growth of the Human Microbiome Project (HMP) opens new horizons for studying how microbiome compositional and functional variations affect drug action, fate, and toxicity (pharmacomicrobiomics), notably in the human gut. The HMP continues to characterize the microbial communities associated with the human gut, determine whether there is a common gut microbiome profile shared among healthy humans, and investigate the effect of its alterations on health. Here, we offer a glimpse into the known effects of the gut microbiota on xenobiotic metabolism, with emphasis on cases where microbiome variations lead to different therapeutic outcomes. We discuss a few examples representing how the microbiome interacts with human metabolic enzymes in the liver and intestine. In addition, we attempt to envisage a roadmap for the future implications of the HMP on therapeutics and personalized medicine

Gut Pharmacomicrobiomics: the tip of an iceberg of complex interactions between drugs and gut-associated microbes

Abstract The influence of resident gut microbes on xenobiotic metabolism has been investigated at different levels throughout the past five decades. However, with the advance in sequencing and pyrotagging technologies, addressing the influence of microbes on xenobiotics had to evolve from assessing direct metabolic effects on toxins and botanicals by conventional culture-based techniques to elucidating the role of community composition on drugs metabolic profiles through DNA sequence-based phylogeny and metagenomics. Following the completion of the Human Genome Project, the rapid, substantial growth of the Human Microbiome Project (HMP) opens new horizons for studying how microbiome compositional and functional variations affect drug action, fate, and toxicity (pharmacomicrobiomics), notably in the human gut. The HMP continues to characterize the microbial communities associated with the human gut, determine whether there is a common gut microbiome profile shared among healthy humans, and investigate the effect of its alterations on health. Here, we offer a glimpse into the known effects of the gut microbiota on xenobiotic metabolism, with emphasis on cases where microbiome variations lead to different therapeutic outcomes. We discuss a few examples representing how the microbiome interacts with human metabolic enzymes in the liver and intestine. In addition, we attempt to envisage a roadmap for the future implications of the HMP on therapeutics and personalized medicine.</p

Deciphering Patterns of Adaptation and Acclimation in the Transcriptome of Phaeocystis antarctica

© 2020 The Authors. Journal of Phycology published by Wiley Periodicals, Inc. on behalf of Phycological Society of America The haptophyte Phaeocystis antarctica is endemic to the Southern Ocean, where iron supply is sporadic and its availability limits primary production. In iron fertilization experiments, P. antarctica showed a prompt and steady increase in cell abundance compared to heavily silicified diatoms along with enhanced colony formation. Here we utilized a transcriptomic approach to investigate molecular responses to alleviation of iron limitation in P. antarctica. We analyzed the transcriptomic response before and after (14 h, 24 h and 72 h) iron addition to a low-iron acclimated culture. After iron addition, we observed indicators of a quick reorganization of cellular energetics, from carbohydrate catabolism and mitochondrial energy production to anabolism. In addition to typical substitution responses from an iron-economic toward an iron-sufficient state for flavodoxin (ferredoxin) and plastocyanin (cytochrome c6), we found other genes utilizing the same strategy involved in nitrogen assimilation and fatty acid desaturation. Our results shed light on a number of adaptive mechanisms that P. antarctica uses under low iron, including the utilization of a Cu-dependent ferric reductase system and indication of mixotrophic growth. The gene expression patterns underpin P. antarctica as a quick responder to iron addition

Deciphering Patterns of Adaptation and Acclimation in the Transcriptome of Phaeocystis antarctica to Changing Iron Conditions

© 2020 The Authors. Journal of Phycology published by Wiley Periodicals, Inc. on behalf of Phycological Society of America The haptophyte Phaeocystis antarctica is endemic to the Southern Ocean, where iron supply is sporadic and its availability limits primary production. In iron fertilization experiments, P. antarctica showed a prompt and steady increase in cell abundance compared to heavily silicified diatoms along with enhanced colony formation. Here we utilized a transcriptomic approach to investigate molecular responses to alleviation of iron limitation in P. antarctica. We analyzed the transcriptomic response before and after (14 h, 24 h and 72 h) iron addition to a low-iron acclimated culture. After iron addition, we observed indicators of a quick reorganization of cellular energetics, from carbohydrate catabolism and mitochondrial energy production to anabolism. In addition to typical substitution responses from an iron-economic toward an iron-sufficient state for flavodoxin (ferredoxin) and plastocyanin (cytochrome c6), we found other genes utilizing the same strategy involved in nitrogen assimilation and fatty acid desaturation. Our results shed light on a number of adaptive mechanisms that P. antarctica uses under low iron, including the utilization of a Cu-dependent ferric reductase system and indication of mixotrophic growth. The gene expression patterns underpin P. antarctica as a quick responder to iron addition

Aufdeckung von Arzneimittelrisiken nach der Zulassung - Methodenentwicklung zur Nutzung von Routinedaten der gesetzlichen Krankenversicherungen

Adverse drug reactions are among the leading causes of death. Pharmacovigilance aims to monitor drugs after they have been released to the market in order to detect potential risks. Data sources commonly used to this end are spontaneous reports sent in by doctors or pharmaceutical companies. Reports alone are rather limited when it comes to detecting potential health risks. Routine statutory health insurance data, however, are a richer source since they not only provide a detailed picture of the patients’ wellbeing over time, but also contain information on concomitant medication and comorbidities. To take advantage of their potential and to increase drug safety, we will further develop statistical methods that have shown their merit in other fields as a source of inspiration. A plethora of methods have been proposed over the years for spontaneous reporting data: a comprehensive comparison of these methods and their potential use for longitudinal data should be explored. In addition, we show how methods from machine learning could aid in identifying rare risks. We discuss these so-called enrichment analyses and how utilizing pharmaceutical similarities between drugs and similarities between comorbidities could help to construct risk profiles of the patients prone to experience an adverse drug event. Summarizing these methods will further push drug safety research based on healthcare claim data from German health insurances which form, due to their size, longitudinal coverage, and timeliness, an excellent basis for investigating adverse effects of drugs.Unerwünschte Arzneimittelwirkungen zählen zu den häufigen Todesursachen. Aufgabe der Pharmakovigilanz ist es, Arzneimittel nach der Zulassung zu überwachen, um so mögliche Risiken aufzudecken. Zu diesem Zweck werden typischerweise Spontanmelderegister genutzt, an die u. a. Ärzte und pharmazeutische Industrie Berichte über unerwünschte Arzneimittelwirkungen (UAW) melden. Diese Register sind jedoch nur begrenzt geeignet, um potenzielle Sicherheitsrisiken zu identifizieren. Eine andere, möglicherweise informativere Datenquelle sind Abrechnungsdaten der gesetzlichen Krankenversicherungen (GKV), die nicht nur den Gesundheitszustand eines Patienten im Längsschnitt erfassen, sondern auch Informationen zu Begleitmedikationen und Komorbiditäten bereitstellen. Um deren Potenzial nutzen zu können und so zur Verbesserung der Arzneimittelsicherheit beizutragen, sollen statistische Methoden weiterentwickelt werden, die sich in anderen Anwendungsgebieten bewährt haben. So steht eine große Bandbreite von Methoden für die Auswertung von Spontanmeldedaten zur Verfügung: Diese sollen zunächst umfassend verglichen und anschließend hinsichtlich ihrer Nutzbarkeit für longitudinale Daten erschlossen werden. Des Weiteren wird aufgezeigt, wie maschinelle Lernverfahren helfen könnten, seltene Risiken zu identifizieren. Zudem werden sogenannte Enrichment-Analysen eingesetzt, mit denen pharmakologische Arzneimittelgruppen und verwandte Komorbiditäten zusammengefasst werden können, um vulnerable Bevölkerungsgruppen zu identifizieren. Insgesamt werden diese Methoden die Arzneimittelrisikoforschung anhand von GKV-Routinedaten vorantreiben, die aufgrund ihres Umfangs, der longitudinalen Erfassung sowie ihrer Aktualität eine vielversprechende Datenquelle bieten, um UAWs aufzudecken

The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole.

Eukaryotic phytoplankton are responsible for at least 20% of annual global carbon fixation. Their diversity and activity are shaped by interactions with prokaryotes as part of complex microbiomes. Although differences in their local species diversity have been estimated, we still have a limited understanding of environmental conditions responsible for compositional differences between local species communities on a large scale from pole to pole. Here, we show, based on pole-to-pole phytoplankton metatranscriptomes and microbial rDNA sequencing, that environmental differences between polar and non-polar upper oceans most strongly impact the large-scale spatial pattern of biodiversity and gene activity in algal microbiomes. The geographic differentiation of co-occurring microbes in algal microbiomes can be well explained by the latitudinal temperature gradient and associated break points in their beta diversity, with an average breakpoint at 14 °C ± 4.3, separating cold and warm upper oceans. As global warming impacts upper ocean temperatures, we project that break points of beta diversity move markedly pole-wards. Hence, abrupt regime shifts in algal microbiomes could be caused by anthropogenic climate change