1,448 research outputs found
Deciduous enamel 3D microwear texture analysis as an indicator of childhood diet in medieval Canterbury, England
This study conducted the first three dimensional microwear texture analysis of human deciduous teeth to reconstruct the physical properties of medieval childhood diet (age 1-8yrs) at St Gregory's Priory and Cemetery (11th to 16th century AD) in Canterbury, England. Occlusal texture complexity surfaces of maxillary molars from juvenile skeletons (n=44) were examined to assess dietary hardness. Anisotropy values were calculated to reconstruct dietary toughness, as well as jaw movements during chewing. Evidence of weaning was sought, and variation in the physical properties of food was assessed against age and socio-economic status. Results indicate that weaning had already commenced in the youngest children. Diet became tougher from four years of age, and harder from age six. Variation in microwear texture surfaces was related to historical textual evidence that refers to lifestyle developments for these age groups. Diet did not vary with socio-economic status, which differs to previously reported patterns for adults. We conclude, microwear texture analyses can provide a non-destructive tool for revealing subtle aspects of childhood diet in the past
Prioritising genetic findings for drug target identification and validation
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation
Exploring the Role of Plasma Lipids and Statins Interventions on Multiple Sclerosis Risk and Severity: A Mendelian Randomization Study
BACKGROUND: There has been considerable interest in statins due to their pleiotropic effects beyond their lipid-lowering properties. Many of these pleiotropic effects are predominantly ascribed to Rho small guanosine triphosphatases (Rho GTPases) proteins. We aimed to genetically investigate the role of lipids and statin interventions on multiple sclerosis (MS) risk and severity. METHOD: We employed two-sample Mendelian randomization (MR) to investigate: (1) the causal role of genetically mimic both cholesterol-dependent (via low-density lipoprotein cholesterol (LDL-C) and cholesterol biosynthesis pathway) and cholesterol-independent (via Rho GTPases) effects of statins on MS risk and MS severity, (2) the causal link between lipids (high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG)) levels and MS risk and severity; and (3) the reverse causation between lipid fractions and MS risk. We used summary statistics from the Global Lipids Genetics Consortium (GLGC), eQTLGen Consortium and the International MS Genetics Consortium (IMSGC) for lipids, expression quantitative trait loci and MS, respectively (GLGC: n = 188,577; eQTLGen: n = 31,684; IMSGC (MS risk): n = 41,505; IMSGC (MS severity): n =7,069). RESULTS: The results of MR using the inverse variance weighted method show that genetically predicted RAC2, a member of cholesterol-independent pathway, (OR 0.86 (95% CI 0.78 to 0.95), p-value 3.80E-03) is implicated causally in reducing MS risk. We found no evidence for the causal role of LDL-C and the member of cholesterol biosynthesis pathway on MS risk. MR results also show that lifelong higher HDL-C (OR 1.14 (95% CI 1.04 to1.26), p-value 7.94E-03) increase MS risk but TG was not. Furthermore, we found no evidence for the causal role of lipids and genetically mimicked statins on MS severity. There is no evidence of reverse causation between MS risk and lipids. CONCLUSION: Evidence from this study suggests that RAC2 is a genetic modifier of MS risk. Since RAC2 has been reported to mediate some of the pleiotropic effects of statins, we suggest that statins may reduce MS risk via a cholesterol-independent pathway (i.e., RAC2-related mechanism(s)). MR analyses also support a causal effect of HDL-C on MS risk
Cosmological Results from High-z Supernovae
The High-z Supernova Search Team has discovered and observed 8 new supernovae
in the redshift interval z=0.3-1.2. These independent observations, confirm the
result of Riess et al. (1998a) and Perlmutter et al. (1999) that supernova
luminosity distances imply an accelerating universe. More importantly, they
extend the redshift range of consistently observed SN Ia to z~1, where the
signature of cosmological effects has the opposite sign of some plausible
systematic effects. Consequently, these measurements not only provide another
quantitative confirmation of the importance of dark energy, but also constitute
a powerful qualitative test for the cosmological origin of cosmic acceleration.
We find a rate for SN Ia of 1.4+/-0.5E-04 h^3/Mpc^3/yr at a mean redshift of
0.5. We present distances and host extinctions for 230 SN Ia. These place the
following constraints on cosmological quantities: if the equation of state
parameter of the dark energy is w=-1, then H0 t0 = 0.96+/-0.04, and O_l - 1.4
O_m = 0.35+/-0.14. Including the constraint of a flat Universe, we find O_m =
0.28+/-0.05, independent of any large-scale structure measurements. Adopting a
prior based on the 2dF redshift survey constraint on O_m and assuming a flat
universe, we find that the equation of state parameter of the dark energy lies
in the range -1.48-1,
we obtain w<-0.73 at 95% confidence. These constraints are similar in precision
and in value to recent results reported using the WMAP satellite, also in
combination with the 2dF redshift survey.Comment: 50 pages, AAS LateX, 15 figures, 15 tables. Accepted for publication
by Astrophysical Journa
Dynamical Structure Factor for the Alternating Heisenberg Chain: A Linked Cluster Calculation
We develop a linked cluster method to calculate the spectral weights of
many-particle excitations at zero temperature. The dynamical structure factor
is expressed as a sum of exclusive structure factors, each representing
contributions from a given set of excited states. A linked cluster technique to
obtain high order series expansions for these quantities is discussed. We apply
these methods to the alternating Heisenberg chain around the dimerized limit
(), where complete wavevector and frequency dependent spectral
weights for one and two-particle excitations (continuum and bound-states) are
obtained. For small to moderate values of the inter-dimer coupling parameter
, these lead to extremely accurate calculations of the dynamical
structure factors. We also examine the variation of the relative spectral
weights of one and two-particle states with bond alternation all the way up to
the limit of the uniform chain (). In agreement with Schmidt and
Uhrig, we find that the spectral weight is dominated by 2-triplet states even
at , which implies that a description in terms of triplet-pair
excitations remains a good quantitative description of the system even for the
uniform chain.Comment: 26 pages, 17 figure
Cell membrane softening in human breast and cervical cancer cells
Biomechanical properties are key to many cellular functions such as cell division and cell motility and
thus are crucial in the development and understanding of several diseases, for instance cancer. The
mechanics of the cellular cytoskeleton have been extensively characterized in cells and artificial
systems. The rigidity of the plasma membrane, with the exception of red blood cells, is unknown and
membrane rigidity measurements only exist for vesicles composed of a few synthetic lipids. In this
study, thermal fluctuations of giant plasma membrane vesicles (GPMVs) directly derived from the
plasma membranes of primary breast and cervical cells, as well as breast cell lines, are analyzed. Cell
blebs or GPMVs were studied via thermal membrane fluctuations and mass spectrometry. It will be
shown that cancer cell membranes are significantly softer than their non-malignant counterparts. This
can be attributed to a loss of fluid raft forming lipids in malignant cells. These results indicate that the
reduction of membrane rigidity promotes aggressive blebbing motion in invasive cancer cells
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