a lower abundance of two species from the family
Rikenellaceae, Alistipes finegoldii, and Alistipes
senegalensis, whereas blood lipids were associated
with two other species, Alistipes shahii and Alistipes
putredinis (table S11). Notably, these species were
also associated to certain dietary factors and drugs.
For instance, a high level of A. shahii, which was
associated to low triglyceride (TG) levels, was linked
to higher fruit intake (q = 0.00027). Individuals
with a higher abundance of A. shahii had a higher
number of different species in the gut (species richness) (Spearman r = 0.2, adjusted P = 3.96x10−11),
suggesting a beneficial effect on the microbial
ecosystem (table S18). Correlations with the number of different species were also found for other
bacteria, including Roseburia hominis, Coprococcus catus, and Barnesiella intestinihominis and
unclassified species from genus Anaerotruncus
that also showed correlation both with fruit, vegetable, and nut consumption and with intrinsic
phenotypes like HDL, triglycerides, and quality
of life. On the basis of these data, it would be
interesting to explore the potential to modulate
disease-associated species through medication
or diet, although we still need to address the
causality and underlying mechanism.
Our study revealed significant associations between the gut microbiome and various intrinsic,
environmental, dietary and medication parameters, and disease phenotypes, with a high replication rate between MGS and 16S rRNA gene
sequencing data from the same individuals. Moreover, our study provides many new intrinsic and
exogenous factors that correlate with shifts in
the microbiome composition and functionality
that potentially can be manipulated to improve
microbiome-related health, and we hope our results will inspire further experiments to explore
the biological relevance of associated factors. Although most of the factors that we assessed exerted
a very modest effect, fecal levels of CgA showed a
high potential as a biomarker for gut health.
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We thank the LifeLines-DEEP participants and the Groningen
LifeLines staff for their collaboration. We thank J. Dekens,
M. Platteel, and A. Maatman for management and technical
support. We thank J. Senior and K. McIntyre for editing the
manuscript. This project was funded by grants from the Top
Institute Food and Nutrition, Wageningen, to C. W. (TiFN GH001);
the Netherlands Organization for Scientific Research to
J.F. (NWO-VIDI 864.13.013), L.F. (ZonMW-VIDI 917.14.374),
and R.K. W. (ZonMW-VIDI 016.136.308); and CardioVasculair
Onderzoek Nederland to M.H.H. and A.Z. (CVON 2012-03). A.Z.
holds a Rosalind Franklin Fellowship (University of Groningen), and
M.C.C. holds a postdoctoral fellowship from the Fundación
Alfonso Martín Escudero. This research received funding from
the European Research Council (ERC) under the European
Union’s Seventh Framework Program: C. W. is supported by
FP7/2007-2013)/ERC advanced Grant Agreement no. 2012-322698.
M.G.N. is supported by an ERC Consolidator Grant (no. 310372).
L.F. is supported by FP7/2007–2013, grant agreement 259867,
and by an ERC Starting Grant, grant agreement 637640
(ImmRisk). J.R. and G.F. are supported by FP7 METACARDIS
HEALTH-F4-2012-305312, VIB, FWO, IWT (Agency for Innovation
by Science and Technology), the Rega institute for Medical
Research, and KU Leuven. S.V.-S. and M.J. are supported by
postdoctoral fellowships from FWO. T.V., M.S., and R.J.X. are
supported by NIH, JDRF, and CCFA. A.Z., C. W., and J.F. designed
the study. A.Z., E.F. T., L.F., and C. W. initiated the cohort and
collected cohort data. A.Z., E.F. T., Z.M., S.A.J., M.C.C., and D.G.
generated data. A.Z., A.K., M.J.B., E.F. T., M.S., T.V., A.V.V., G.F.,
S.V.-S., J. W., F.I., P.D., M.A.S., C.H., R.J.X., and J.F. analyzed data.
G.F, S.V.-S., J. W., E.B., M.J., R.K. W., E.J.M.F., M.G.N., D.G., D.J.,
L.F., Y.S.A., C.H., J.R., R.J.X., and M.H.H. participated in integral
discussions. A.Z., A.K., M.J.B., R.J.X., C. W., and J.F. wrote the
manuscript. The authors have no conflicts of interest to report.
The raw sequence data for both MGS and 16S rRNA gene
sequencing data sets, and age and gender information per sample
are available from the European genome-phenome archive
( https://www.ebi.ac.uk/ega/) at accession number EGAS00001001704.
Other phenotypic data can be requested from the LifeLines
cohort study ( https://lifelines.nl/lifelines-research/access-to-lifelines)
following the standard protocol for data access. The study was
approved by the institutional review board of UMCG, ref.M12.
113965. D.J. has additional funding from EU FP7/ no. 305564 and
EU FP7/ no. 305479. C.H. is on the Scientific Advisory Board for
Seres Therapeutics. Y.S.A. is a director and co-owner of
PolyOmica, which provides services in statistical (gen)omics.
Materials and Methods
Figs. S1 to S13
Tables S1 to S19
1 September 2015; accepted 11 March 2016
Kinetically controlled E-selective
catalytic olefin metathesis
Thach T. Nguyen,1 Ming Joo Koh,1 Xiao Shen,1 Filippo Romiti,1
Richard R. Schrock,2 Amir H. Hoveyda1*
A major shortcoming in olefin metathesis, a chemical process that is central to research
in several branches of chemistry, is the lack of efficient methods that kinetically favor E
isomers in the product distribution. Here we show that kinetically E-selective cross-metathesis
reactions may be designed to generate thermodynamically disfavored alkenyl chlorides
and fluorides in high yield and with exceptional stereoselectivity. With 1.0 to 5.0 mole %
of a molybdenum-based catalyst, which may be delivered in the form of air- and
moisture-stable paraffin pellets, reactions typically proceed to completion within 4 hours at
ambient temperature. Many isomerically pure E-alkenyl chlorides, applicable to catalytic
cross-coupling transformations and found in biologically active entities, thus become easily
and directly accessible. Similarly, E-alkenyl fluorides can be synthesized from simpler
compounds or more complex molecules.
Olefin metathesis is an enormously enabling chemical process for which well-defined catalysts were discovered nearly three decades ago (1, 2). Kinetically controlled Z-selective reactions were introduced in
2009 (3), but there are no corresponding transformations that are broadly applicable and through
which E isomers can be synthesized in high yield.
Although E-selective cross-metathesis (CM) reactions involving Ru catechothiolate complexes
(4) were reported very recently in 2016, only
the thermodynamically preferred E isomers of
simple (unfunctionalized) 1,2-disubstituted aliphatic alkenes could be obtained in 3 to 31% yield
(5). E alkenes are often lower in energy and thus
generated preferentially; nonetheless, olefin metathesis strategies that furnish them are needed
for several reasons: The energy gap between the
geometric forms is often too small to ensure
high selectivity; E olefin isomers are not always
thermodynamically preferred; and, in many cases,
SCIENCE sciencemag.org 29 APRIL 2016 • VOL 352 ISSUE 6285 569
1Department of Chemistry, Merkert Chemistry Center, Boston
College, Chestnut Hill, MA 02467, USA. 2Department of
Chemistry, Massachusetts Institute of Technology,
Cambridge, MA 02139, USA.
*Corresponding author. Email: firstname.lastname@example.org
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