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Abstract Human microbiome composition is closely tied to health, but how the host manages its microbial inhabitants remains unclear. One important, but understudied, factor is the natural host environment: mucus, which contains gel-forming glycoproteins (mucins) that display hundreds of glycan structures with potential regulatory function. Leveraging a tractable culture-based system to study how mucins influence oral microbial communities, we found that mucin glycans enable the coexistence of diverse microbes, while resisting disease-associated compositional shifts. Mucins from tissues with unique glycosylation differentially tuned microbial composition, as did isolated mucin glycan libraries, uncovering the importance of specific glycan patterns in microbiome modulation. We found that mucins shape microbial communities in several ways: serving as nutrients to support metabolic diversity, organizing spatial structure through reduced aggregation, and possibly limiting antagonism between competing taxa. Overall, this work identifies mucin glycans as a natural host mechanism and potential therapeutic intervention to maintain healthy microbial communities. 

This talk will describe the work of the CPN Pre-Impact Baselines Working Group to leverage the wealth of paleoecological and historical ecological data to facilitate estimation of pre-impact species distribution baselines. Species conservation has long focused on preventing human-driven extinctions, and over the past 50 years conservation success has been measured using changes in species’ extinction risk. However, recently calls have been made for a parallel focus on species recovery, and on developing metrics with which to assess its achievement. This call to action within the conservation community is fuelled in part by the recognition that baselines of species abundance and distribution have shifted dramatically across human generations with globally detectable human impacts on ecosystems beginning at least several thousand years ago. While assessment of extinction risk generally only considers species’ change over the past few decades, assessment of recovery requires considering change over centuries to millennia. This requires identifying the baseline status at the time when humans first became a major factor influencing the abundance and distribution of a species. Two new frameworks for considering conservation status relative to a species’ pre-impact baseline have been recently released: EPOCH (Evaluation of POpulation CHange), and the IUCN Green Status of Species. These frameworks have been lauded as moving conservation in a much-needed direction, but there is also concern about whether these methods will be applicable to any but a few well-known, charismatic species. Using a combination of modelling approaches, we are working to estimate species pre-impact distributions in a way that is accessible to conservation practitioners, helping to unshift the baseline and bring species recovery into the mainstream. 

ABSTRACT The European common cuttlefish, Sepia officinalis , is used extensively in biological and biomedical research, yet its microbiome remains poorly characterized. We analyzed the microbiota of the digestive tract, gills, and skin in mariculture-raised S. officinalis using a combination of 16S rRNA amplicon sequencing, quantitative PCR (qPCR), and fluorescence spectral imaging. Sequencing revealed a highly simplified microbiota consisting largely of two single bacterial amplicon sequence variants (ASVs) of Vibrionaceae and Piscirickettsiaceae . The esophagus was dominated by a single ASV of the genus Vibrio . Imaging revealed bacteria in the family Vibrionaceae distributed in a discrete layer that lines the esophagus. This Vibrio was also the primary ASV found in the microbiota of the stomach, cecum, and intestine, but occurred at lower abundance, as determined by qPCR, and was found only scattered in the lumen rather than in a discrete layer via imaging analysis. Treatment of animals with the commonly used antibiotic enrofloxacin led to a nearly 80% reduction of the dominant Vibrio ASV in the esophagus but did not significantly alter the relative abundance of bacteria overall between treated versus control animals. Data from the gills were dominated by a single ASV in the family Piscirickettsiaceae , which imaging visualized as small clusters of cells. We conclude that bacteria belonging to the Gammaproteobacteria are the major symbionts of the cuttlefish Sepia officinalis cultured from eggs in captivity and that the esophagus and gills are major colonization sites. IMPORTANCE Microbes can play critical roles in the physiology of their animal hosts, as evidenced in cephalopods by the role of Vibrio ( Aliivibrio ) fischeri in the light organ of the bobtail squid and the role of Alpha - and Gammaproteobacteria in the reproductive system and egg defense in a variety of cephalopods. We sampled the cuttlefish microbiome throughout the digestive tract, gills, and skin and found dense colonization of an unexpected site, the esophagus, by a microbe of the genus Vibrio , as well as colonization of gills by Piscirickettsiaceae . This finding expands the range of organisms and body sites known to be associated with Vibrio and is of potential significance for understanding host-symbiont associations, as well as for understanding and maintaining the health of cephalopods in mariculture. 

Abstract MotivationSpectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images. ResultsWe propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error. Availability and implementationThe source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.