Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows

We thank Mari Talvisilta and the staff in the metabolism unit at Natural Resources Institute Finland for technical support, care of experimental animals and assistance in sample collection. We thank Paula Lidauer for ruminal cannulation surgeries, Richard Hill from Aberystwyth University, UK for performing qPCR and Aurélie Bonin from Laboratoire d'Ecologie Alpine, CNRS, France for preparing archaea amplicon libraries for sequencing. Kevin J. Shingfield passed away before the submission of the final version of this manuscript. Ilma Tapio accepts responsibility for the integrity and validity of the data collected and analyzed. Funding: Study was funded by the Finnish Ministry of Agriculture and Forestry as part of the GreenDairy Project (Developing Genetic and Nutritional Tools to Mitigate the Environmental Impact of Milk Production; Project No. 2908234). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Intertidal percolation through beach sands as a source of 224,223 Ra to Long Island Sound, New York, and Connecticut, United States

Along tidal coasts, seawater circulated through the intertidal beach contributes to submarine groundwater discharge (SGD) and its associated geochemical signature. The short-lived radium isotopes, 223Ra (half-life = 11.4 d) and 224Ra (half-life = 3.66 d), were used to quantify this component of SGD in a large estuary, Long Island Sound (LIS), New York, United States. The tide is semidiurnal with a range of approximately 2 m. Concentrations in beach pore waters ranged from 97 to 678 disintegrations per minute (dpm) 224Ra 100 L–1, whereas concentrations in open coastal waters ranged from approximately 12 to 69 dpm 224Ra 100 L–1. A simple model based on ingrowth of 224Ra in the pore water of the beach sands was used to determine residence times of 0.6 to 2.5 d for water in the intertidal beach. Both 223Ra and 224Ra showed decreasing gradients and concentration in an offshore transect away from the beach face in Smithtown Bay, whereas the long-lived radium isotopes, 228Ra (half-life = 5.75 y) and 226Ra (half-life = 1,600 y), showed no significant gradients. Based on the 224Ra gradient, the flux across the LIS shoreline was estimated to be 1.79 × 108 dpm m–1 y–1. The 224Ra inventories in two zones, 0–50 m and 0–100 m offshore, were used to estimate total SGD fluxes of 3.1 × 1010 to 6.6 × 1010 m3 y–1 of intertidal seawater to the nearshore of LIS. Comparison of this estimate with hydrodynamic models of fresh groundwater flow in the adjacent coastal aquifer suggests that less than 1% of the SGD is freshwater

Exercise-Induced Bone Formation Is Poorly Linked to Local Strain Magnitude in the Sheep Tibia

Functional interpretations of limb bone structure frequently assume that diaphyses adjust their shape by adding bone primarily across the plane in which they are habitually loaded in order to minimize loading-induced strains. Here, to test this hypothesis, we characterize the in vivo strain environment of the sheep tibial midshaft during treadmill exercise and examine whether this activity promotes bone formation disproportionately in the direction of loading in diaphyseal regions that experience the highest strains. It is shown that during treadmill exercise, sheep tibiae were bent in an anteroposterior direction, generating maximal tensile and compressive strains on the anterior and posterior shaft surfaces, respectively. Exercise led to significantly increased periosteal bone formation; however, rather than being biased toward areas of maximal strains across the anteroposterior axis, exercise-related osteogenesis occurred primarily around the medial half of the shaft circumference, in both high and low strain regions. Overall, the results of this study demonstrate that loading-induced bone growth is not closely linked to local strain magnitude in every instance. Therefore, caution is necessary when bone shaft shape is used to infer functional loading history in the absence of in vivo data on how bones are loaded and how they actually respond to loading

Beyond Nanopore Sequencing in Space: Identifying the Unknown

Astronaut Kate Rubins sequenced DNA on the International Space Station (ISS) for the first time in August 2016 (Figure 1A). A 2D sequencing library containing an equal mixture of lambda bacteriophage, Escherichia coli, and Mus musculus was prepared on the ground with a SQK_MAP006 kit and sent to the ISS frozen and loaded into R7.3 flow cells. After a total of 9 on-orbit sequencing runs over 6 months, it was determined that there was no decrease in sequencing performance on-orbit compared to ground controls (1). A total of ~280,000 and ~130,000 reads generated on-orbit and on the ground, respectively, identified 90% of reads that were attributed to 30% lambda bacteriophage, 30% Escherichia coli, and 30% M. musculus (Figure 1B). Extensive bioinformatics analysis determined comparable 2D and 1D read accuracies between flight and ground runs (Figure 1C), and data collected from the ISS were able to construct directed assemblies of E.coli and lambda genomes at 100% and M. musculus mitochondrial genome at 96.7%. These findings validate sequencing as a viable option for potential on-orbit applications such as environmental microbial monitoring and disease diagnosis. Current microbial monitoring of the ISS applies culture-based techniques that provide colony forming unit (CFU) data for air, water, and surface samples. The identity of the cultured microorganisms in unknown until sample return and ground-based analysis, a process that can take up to 60 days. For sequencing to benefit ISS applications, spaceflight-compatible sample preparation techniques are required. Subsequent to the testing of the MinION on-orbit, a sample-to-sequence method was developed using miniPCR and basic pipetting, which was only recently proven to be effective in microgravity. The work presented here details the in- flight sample preparation process and the first application of DNA sequencing on the ISS to identify unknown ISS-derived microorganisms

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

BACKGROUND: The ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information. RESULTS: Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories. Via Biofilter 2.0 researchers can: • Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation • Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest • Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing. CONCLUSIONS: Biofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes

High plasma leptin levels confer increased risk of atherosclerosis in women with systemic lupus erythematosus, and are associated with inflammatory oxidised lipids.

BackgroundPatients with systemic lupus erythematosus (SLE) are at increased risk of atherosclerosis, even after accounting for traditional risk factors. High levels of leptin and low levels of adiponectin are associated with both atherosclerosis and immunomodulatory functions in the general population.ObjectiveTo examine the association between these adipokines and subclinical atherosclerosis in SLE, and also with other known inflammatory biomarkers of atherosclerosis.MethodsCarotid ultrasonography was performed in 250 women with SLE and 122 controls. Plasma leptin and adiponectin levels were measured. Lipoprotein a (Lp(a)), oxidised phospholipids on apoB100 (OxPL/apoB100), paraoxonase, apoA-1 and inflammatory high-density lipoprotein (HDL) function were also assessed.ResultsLeptin levels were significantly higher in patients with SLE than in controls (23.7±28.0 vs 13.3±12.9 ng/ml, p<0.001). Leptin was also higher in the 43 patients with SLE with plaque than without plaque (36.4±32.3 vs 20.9±26.4 ng/ml, p=0.002). After multivariate analysis, the only significant factors associated with plaque in SLE were leptin levels in the highest quartile (≥29.5 ng/ml) (OR=2.8, p=0.03), proinflammatory HDL (piHDL) (OR=12.8, p<0.001), age (OR=1.1, p<0.001), tobacco use (OR=7.7, p=0.03) and hypertension (OR=3.0, p=0.01). Adiponectin levels were not significantly associated with plaque in our cohort. A significant correlation between leptin and piHDL function (p<0.001), Lp(a) (p=0.01) and OxPL/apoB100 (p=0.02) was also present.ConclusionsHigh leptin levels greatly increase the risk of subclinical atherosclerosis in SLE, and are also associated with an increase in inflammatory biomarkers of atherosclerosis such as piHDL, Lp(a) and OxPL/apoB100. High leptin levels may help to identify patients with SLE at risk of atherosclerosis

Oral Samples as Non-Invasive Proxies for Assessing the Composition of the Rumen Microbial Community

Microbial community analysis was carried out on ruminal digesta obtained directly via rumen fistula and buccal fluid, regurgitated digesta (bolus) and faeces of dairy cattle to assess if non-invasive samples could be used as proxies for ruminal digesta. Samples were collected from five cows receiving grass silage based diets containing no additional lipid or four different lipid supplements in a 5 x 5 Latin square design. Extracted DNA was analysed by qPCR and by sequencing 16S and 18S rRNA genes or the fungal ITS1 amplicons. Faeces contained few protozoa, and bacterial, fungal and archaeal communities were substantially different to ruminal digesta. Buccal and bolus samples gave much more similar profiles to ruminal digesta, although fewer archaea were detected in buccal and bolus samples. Bolus samples overall were most similar to ruminal samples. The differences between both buccal and bolus samples and ruminal digesta were consistent across all treatments. It can be concluded that either proxy sample type could be used as a predictor of the rumen microbial community, thereby enabling more convenient large-scale animal sampling for phenotyping and possible use in future animal breeding programs aimed at selecting cattle with a lower environmental footprint

Concurrent Machine learning Assisted Raman Spectroscopy of Whole Blood and Saliva for Breast Cancer Diagnostics

Highly sensitive and unique biomarkers are needed for early cancer detection. In particular, biomarkers in biofluids can be useful in detecting the existence of a tumor early in the body. The utility of biofluid markers for cancer detection can be enhanced when multiple biofluids are simultaneously biochemically analyzed in order to acquire complementary information for diagnostic purposes. This work aimed at investigating the universal human whole blood and saliva biomarkers for breast cancer screening using machine learning-assisted Raman spectroscopy. Raman spectroscopy was performed in the 393 – 2063 cm-1 region using 785 nm laser excitation. Machine learning-assisted Raman spectroscopy was implemented by performing principal component analysis, independent component analysis, and support vector machine modeling on the Raman spectra in order to extract the underlying multivariate relationships between the observed biochemical alterations. Ten spectral regions were determined: 612 ± 1.44 cm-1, 785 cm-1, 968 ± 2.02 cm-1, 1000 ± 0.86 cm-1, 1248 cm-1, 1340 cm-1, 1371 ± 0.57 cm-1, 1448 ± 1.73 cm-1, 1500 ± 2.88 cm-1, and 1661 ± 1.44 cm-1, which can be regarded as universal biomarkers of breast cancer using both whole blood and saliva samples. The diagnostic models based on principal component analysis followed by support vector machine achieved mean sensitivity of 95.83 ± 2.48%, specificity of 99.16 ± 0.65%, and accuracy of 98.50 ± 0.65% when differentiating healthy blood samples from diseased blood samples. Further, this model yielded mean sensitivity of 73.0 ± 6.20%, specificity of 97.50 ± 0.67%, and accuracy of 93.66 ± 0.80% when differentiating the healthy saliva samples from diseased saliva samples. The determined biomarkers could be used to establish a spectral system for detection of breast cancer. Further work, including large sample sizes, has to be done to figure out how proteins and nucleic acids behave in their conformational states in human blood and saliva before translating the findings to actual clinical application

David Quentin Bowen: A memorial

The Quaternary community lost a giant and a leader on October 5, 2020, when David Quentin Bowen, known to many as “DQ” and founding editor of Quaternary Science Reviews, passed away in Cardiff. Born on February 14, 1938 in Llanelli, SouthWales, he received his PhD at University College London. David’s 50 years of contributions to our science cannot be adequately summarized in a brief memorial but past, present, and future generations of Quaternary scientists will long remember his landmark achievements in publishing, his scientific contributions, and his personal and professional class in all his endeavors

Epigenetic analysis of regulatory T cells using multiplex bisulfite sequencing.

This work was supported by Wellcome Trust Grant 096388, JDRF Grant 9-2011-253, the National Institute for Health Research Cambridge Biomedical Research Centre (BRC) and Award P01AI039671 (to LSW. and JAT.) from the National Institute of Allergy and Infectious Diseases (NIAID). CW is supported by the Wellcome Trust (089989). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of NIAID or the National Institutes of Health. The Cambridge Institute for Medical Research is in receipt of Wellcome Trust Strategic Award 100140. We gratefully acknowledge the participation of all NIHR Cambridge BioResource volunteers. We thank the Cambridge BioResource staff for their help with volunteer recruitment. We thank members of the Cambridge BioResource SAB and Management Committee for their support of our study and the National Institute for Health Research Cambridge Biomedical Research Centre for funding. We thank Fay Rodger and Ruth Littleboy for running the Illumina MiSeq in the Molecular Genetics Laboratories, Addenbrooke's Hospital, Cambridge. This research was supported by the Cambridge NIHR BRC Cell Phenotyping Hub. In particular, we wish to thank Anna Petrunkina Harrison, Simon McCallum, Christopher Bowman, Natalia Savinykh, Esther Perez and Jelena Markovic Djuric for their advice and support in cell sorting. We also thank Helen Stevens, Pamela Clarke, Gillian Coleman, Sarah Dawson, Jennifer Denesha, Simon Duley, Meeta Maisuria-Armer and Trupti Mistry for acquisition and preparation of samples.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/eji.20154564