To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Genetic approaches are increasingly advantageous in characterizing treatment-resistant schizophrenia (TRS). We aimed to identify TRS-associated functional brain proteins, providing a potential pathway for improving psychiatric classification and developing better-tailored therapeutic targets.
TRS-related proteome-wide association studies (PWAS) were conducted on genome-wide association studies (GWAS) from CLOZUK and the Psychiatric Genomics Consortium (PGC), which provided TRS individuals (n = 10,501) and non-TRS individuals (n = 20,325), respectively. The reference datasets for the human brain proteome were obtained from ROS/MAP and Banner, with 8,356 and 11,518 proteins collected, respectively. We then performed colocalization analysis and functional enrichment analysis to further explore the biological functions of the proteins identified by PWAS.
In PWAS, two statistically significant proteins were identified using the ROS/MAP and then replicated using the Banner reference dataset, including CPT2 (PPWAS-ROS/MAP = 4.15 × 10−2 and PPWAS-Banner = 3.38 × 10−3) and APOL2 (PPWAS-ROS/MAP = 4.49 × 10−3 and PPWAS-Banner = 8.26 × 10−3). Colocalization analysis identified three variants that were causally related to protein expression in the human brain, including CCDC91 (PP4 = 0.981), PRDX1 (PP4 = 0.894), and WARS2 (PP4 = 0.757). We extended PWAS results from gene-based analysis to pathway-based analysis, identifying 14 gene ontology (GO) terms and the only candidate pathway for TRS, metabolic pathways (all P < 0.05).
Our results identified two protein biomarkers, and cautiously support that the pathological mechanism of TRS is linked to lipid oxidation and inflammation, where mitochondria-related functions may play a role.
The subduction model of the Neo-Tethys during the Early Cretaceous has always been a controversial topic, and the scarcity of Early Cretaceous magmatic rocks in the southern part of the Gangdese batholith is the main cause of this debate. To address this issue, this article presents new zircon U–Pb chronology, zircon Hf isotope, whole-rock geochemistry and Sr–Nd isotope data for the Early Cretaceous quartz diorite dykes with adakite affinity in Liuqiong, Gongga. Zircon U–Pb dating of three samples yielded ages of c. 141–137 Ma, indicating that the Liuqiong quartz diorite was emplaced in the Early Cretaceous. The whole-rock geochemical analysis shows that the Liuqiong quartz diorite is enriched in large-ion lithophile elements (LILEs) and light rare-earth elements (LREEs) and is depleted in high-field-strength elements (HFSEs), which are related to slab subduction. Additionally, the Liuqiong quartz diorite has high SiO2, Al2O3 and Sr contents, high Sr/Y ratios and low heavy rare-earth element (HREE) and Y contents, which are compatible with typical adakite signatures. The initial 87Sr/86Sr values of the Liuqiong adakite range from 0.705617 to 0.705853, and the whole-rock ϵNd(t) values vary between +5.78 and +6.24. The zircon ϵHf(t) values vary from +11.5 to +16.4. Our results show that the Liuqiong adakite magma was derived from partial melting of the Neo-Tethyan oceanic plate (mid-ocean ridge basalt (MORB) + sediment + fluid), with some degree of subsequent peridotite interaction within the overlying mantle wedge. Combining regional data, we favour the interpretation that the Neo-Tethyan oceanic crust was subducted at a low angle beneath the Gangdese during the Early Cretaceous.
This study evaluated the association between inflammatory diets as measured by the Dietary Inflammatory index (DII), inflammation biomarkers and the development of preeclampsia among the Chinese population. We followed the reporting guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology statement for observational studies. A total of 466 preeclampsia cases aged over 18 years were recruited between March 2016 and June 2019, and 466 healthy controls were 1:1 ratio matched by age (±3 years), week of gestation (±1 week) and gestational diabetes mellitus. The energy-adjusted DII (E-DII) was computed based on dietary intake assessed using a seventy-nine item semiquantitative FFQ. Inflammatory biomarkers were analysed by ELISA kits. The mean E-DII scores were −0·65 ± 1·58 for cases and −1·19 ± 1·47 for controls (P value < 0·001). E-DII scores positively correlated with interferon-γ (rs = 0·194, P value = 0·001) and IL-4 (rs = 0·135, P value = 0·021). After multivariable adjustment, E-DII scores were positively related to preeclampsia risk (Ptrend < 0·001). The highest tertile of E-DII was 2·18 times the lowest tertiles (95 % CI = 1·52, 3·13). The odds of preeclampsia increased by 30 % (95 % CI = 18 %, 43 %, P value < 0·001) for each E-DII score increase. The preeclampsia risk was positively associated with IL-2 (OR = 1·07, 95 % CI = 1·03, 1·11), IL-4 (OR = 1·26, 95 % CI = 1·03, 1·54) and transforming growth factor beta (TGF-β) (OR = 1·17, 95 % CI = 1·06, 1·29). Therefore, proinflammatory diets, corresponding to higher IL-2, IL-4 and TGF-β levels, were associated with increased preeclampsia risk.
Shifts in the maternal gut microbiota have been implicated in the development of gestational diabetes mellitus (GDM). Understanding the interaction between gut microbiota and host glucose metabolism will provide a new target of prediction and treatment. In this nested case-control study, we aimed to investigate the causal effects of gut microbiota from GDM patients on the glucose metabolism of germ-free (GF) mice. Stool and peripheral blood samples, as well as clinical information, were collected from 45 GDM patients and 45 healthy controls (matched by age and prepregnancy body mass index (BMI)) in the first and second trimester. Gut microbiota profiles were explored by next-generation sequencing of the 16S rRNA gene, and inflammatory factors in peripheral blood were analyzed by enzyme-linked immunosorbent assay. Fecal samples from GDM and non-GDM donors were transferred to GF mice. The gut microbiota of women with GDM showed reduced richness, specifically decreased Bacteroides and Akkermansia, as well as increased Faecalibacterium. The relative abundance of Akkermansia was negatively associated with blood glucose levels, and the relative abundance of Faecalibacterium was positively related to inflammatory factor concentrations. The transfer of fecal microbiota from GDM and non-GDM donors to GF mice resulted in different gut microbiota colonization patterns, and hyperglycemia was induced in mice that received GDM donor microbiota. These results suggested that the shifting pattern of gut microbiota in GDM patients contributed to disease pathogenesis.
Email your librarian or administrator to recommend adding this to your organisation's collection.