A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

The microorganisms that inhabit hospitals may significantly influence patient recovery rates and outcomes (REFs). To develop a community level understating of how microorganisms colonize and move through the hospital environment, we mapped microbial dynamics between hospital surfaces, air and water to patients and staff over the course of one year as a new hospital became operational. Immediately following the introduction of staff and patients, the hospital microbiome became dominated by human skin-associated bacteria. Human skin samples had the lowest microbial diversity, while the greatest diversity was found on surfaces interacting with outdoor environments. The microbiota of patient room surfaces, especially bedrails, consistently resembled the skin microbial community of the current patient, with degree of similarity significantly correlated to higher humidity and lower temperatures. Microbial similarity between staff members showed a significant seasonal trend being greatest in late summer/early fall correlating with increased humidity.

The definition of sepsis has been recently modified to accommodate emerging knowledge in the field, while at the same time being recognized as challenging, if not impossible, to define. Here we seek to clarify the current understanding of sepsis as one that has been typically framed as a disorder of inflammation to one in which the competing interests of the microbiota, pathobiota and host immune cells leads to loss of resilience and non-resolving organ dysfunction. Here we challenge the existence of the idea of non-infectious sepsis given that critically ill humans never exist in a germ free state. Finally, we propose a new vision of the pathophysiology of sepsis that includes the invariable loss of the host’s microbiome with the emergence of a pathobiome consisting of both healthcare acquired and healthcare adapted pathobiota. Under this framework, the critically ill patient is viewed as a host colonized by pathobiota dynamically expressing emergent properties which drive, and are driven by, a pathoadaptive immune response.

Sepsis following surgical injury remains a growing and worrisome problem following both emergent and elective surgery. Although early resuscitation efforts and prompt antibiotic therapy have improved outcomes in the first 24–48 hours, late onset sepsis is now the most common cause of death in modern intensive care units. This time shift may be, in part, a result of prolonged exposure of the host to the stressors of critical illness which, over time, erode the health promoting intestinal microbiota and allow for virulent pathogens to predominate. Colonizing pathogens can then subvert the immune system and contribute to the deterioration of the host response. Here we posit that novel approaches integrating the molecular, ecological and evolutionary dynamics of the evolving gut microbiome/pathobiome during critical illness are needed to understand and prevent the late onset sepsis that develops following prolonged critical illness.