Cryptochrome 1 regulates ovarian granulosa cell senescence through NCOA4-mediated ferritinophagy.

Age-associated decreases in follicle number and oocyte quality result in a decline in female fertility, which is associated with increased infertility. Granulosa cells play a major role in oocyte development and maturation both in vivo and in vitro. However, it is unclear whether a reduction in cryptochrome 1 (Cry1) expression contributes to granulosa cell senescence, and further exploration is needed to understand the underlying mechanisms. In this study, we investigated the role of Cry1, a core component of the molecular circadian clock, in the regulation of senescence in ovarian granulosa cells. Western blotting and qRT-PCR showed that Cry1 expression was downregulated in aged human ovarian granulosa cells and was correlated with age and anti-Müllerian hormone (AMH) levels. RNA-seq analysis suggested that ferritinophagy was increased after Cry1 knockdown in KGN cells. MDA, iron, and reactive oxygen species (ROS) assays were used to detect cellular ferritinophagy levels. Ferroptosis inhibitors, iron chelators, autophagy inhibitors, and nuclear receptor coactivator 4 (NCOA4) knockdown alleviated KGN cell senescence induced by Cry1 knockdown. Immunofluorescence, immunoprecipitation, and ubiquitination assays indicated that Cry1 affected NCOA4 ubiquitination and degradation through HERC2, thereby affecting NCOA4-mediated ferritinophagy and causing granulosa cell senescence. KL201, a Cry1 stabilizer, enhanced ovarian function in naturally aged mice by reducing ferritinophagy. Our study reveals the potential mechanisms of action of Cry1 during ovarian aging and provides new insights for the clinical treatment of age-related fertility decline.

Confidence response times: Challenging postdecisional models of confidence.

Even though the nature of confidence computations has been the topic of intense interest, little attention has been paid to what confidence response times (cRTs) reveal about the underlying confidence computations. Several previous studies found cRTs to be negatively correlated with confidence in the group as a whole and consequently hypothesized the existence of an intrinsic relationship of cRT with confidence for all subjects. This hypothesis was further used to support postdecisional models of confidence that predict that cRT and confidence should always be negatively correlated. Here we test the alternative hypothesis that cRT is driven by the frequency of confidence responses such that the most frequent confidence ratings are inherently made faster regardless of whether they are high or low. We examined cRTs in three large data sets from the Confidence Database and found that the lowest cRTs occurred for the most frequent confidence rating. In other words, subjects who gave high confidence ratings most frequently had negative confidence-cRT relationships, whereas subjects who gave low confidence ratings most frequently had positive confidence-cRT relationships. In addition, we found a strong across-subject correlation between response time and cRT, suggesting that response speed for both the decision and the confidence rating is influenced by a common factor. Our results show that cRT is not intrinsically linked to confidence and strongly challenge several postdecisional models of confidence.

Room-Temperature Synthesis of Zeolite Membranes toward Optimized Microstructure and Enhanced Butane Isomer Separation Performance.

Room temperature (RT) synthesis of high-performance zeolite membranes, which is profound from techno-economic and eco-friendly perspectives, remains a grand challenge. In this work, we pioneered the RT preparation of well-intergrown pure-silica MFI zeolite (Si-MFI) membranes, which was realized through adopting highly reactive NH4F-mediated gel as nutrient during epitaxial growth. Benefiting from the introduction of fluoride anions as mineralizing agent as well as precisely tuned nucleation and growth kinetics at RT, both their grain boundary structure and thickness could be deliberately controlled, resulting in the formation of Si-MFI membranes showing unprecedented n-/i-butane separation factor (96.7) and n-butane permeance (5.16 × 10-7 mol m-2 s-1 Pa-1) in the case of a feed molar ratio of 10/90, which well transcended the state-of-the-art membranes reported in the literature. This RT synthetic protocol was also proven effective for preparing highly b-oriented Si-MFI film, thus showing great promise for the preparation of diverse zeolite membranes with optimized microstructure and superior performance.

Fabrication of Highly Oriented Ultrathin Zirconium Metal-Organic Framework Membrane from Nanosheets towards Unprecedented Gas Separation.

Concurrent regulation of crystallographic orientation and thickness of zirconium metal-organic framework (Zr-MOF) membranes is challenging but promising for their performance enhancement. In this study, we pioneered the fabrication of uniform triangular-shaped, 40 nm thick UiO-66 nanosheet (NS) seeds by employing an anisotropic etching strategy. Through innovating confined counter-diffusion-assisted epitaxial growth, highly (111)-oriented 165 nm-thick UiO-66 membrane was prepared. The significant reduction in thickness and diffusion barrier in the framework endowed the membrane with unprecedented CO2 permeance (2070 GPU) as well as high CO2 /N2 selectivity (35.4), which surpassed the performance limits of state-of-the-art polycrystalline MOF membranes. In addition, highly (111)-oriented 180 nm-thick NH2 -UiO-66 membrane showing superb H2 /CO2 separation performance with H2 permeance of 1230 GPU and H2 /CO2 selectivity of 41.3, was prepared with the above synthetic procedure.

High-Valence Metal-Organic Framework Materials Constructed from Metal-Oxo Clusters: Opportunities and Challenges.

Metal-organic framework (MOF), which possesses stable framework structure constructed by highly connected metal-oxo cluster nodes and organic linkers, has shown great promise in gas storage, adsorption, and separation, owing to the high surface areas, tunable pore aperture, and rich functional groups. In this review article, we summarized recent progress made in synthesizing high-valence MOF (e. g., UiO-66, MIL-125, PCN-22, and MIP-207) with metal-oxo cluster as metal source. Of particular note, recent breakthroughs in the preparation of UiO-66 and MIL-125 membranes with the corresponding Zr6 -oxo and Ti8 -oxo cluster sources (e. g., Zr6 O4 (OH)4 (OAc)12 and Ti8 O8 (OOCR)16 clusters) possessing superior separation performance were highlighted. In the end, an outlook on the preparation of versatile high-valence MOF membranes with the corresponding metal-oxo clusters as metal sources was highlighted.

Gene burden analysis identifies genes associated with increased risk and severity of adult-onset hearing loss in a diverse hospital-based cohort.

Loss or absence of hearing is common at both extremes of human lifespan, in the forms of congenital deafness and age-related hearing loss. While these are often studied separately, there is increasing evidence that their genetic basis is at least partially overlapping. In particular, both common and rare variants in genes associated with monogenic forms of hearing loss also contribute to the more polygenic basis of age-related hearing loss. Here, we directly test this model in the Penn Medicine BioBank-a healthcare system cohort of around 40,000 individuals with linked genetic and electronic health record data. We show that increased burden of predicted deleterious variants in Mendelian hearing loss genes is associated with increased risk and severity of adult-onset hearing loss. As a specific example, we identify one gene-TCOF1, responsible for a syndromic form of congenital hearing loss-in which deleterious variants are also associated with adult-onset hearing loss. We also identify four additional novel candidate genes (COL5A1, HMMR, RAPGEF3, and NNT) in which rare variant burden may be associated with hearing loss. Our results confirm that rare variants in Mendelian hearing loss genes contribute to polygenic risk of hearing loss, and emphasize the utility of healthcare system cohorts to study common complex traits and diseases.

Developmental trajectories of thalamic progenitors revealed by single-cell transcriptome profiling and Shh perturbation.

The thalamus is the principal information hub of the vertebrate brain, with essential roles in sensory and motor information processing, attention, and memory. The complex array of thalamic nuclei develops from a restricted pool of neural progenitors. We apply longitudinal single-cell RNA sequencing and regional abrogation of Sonic hedgehog (Shh) to map the developmental trajectories of thalamic progenitors, intermediate progenitors, and post-mitotic neurons as they coalesce into distinct thalamic nuclei. These data reveal that the complex architecture of the thalamus is established early during embryonic brain development through the coordinated action of four cell differentiation lineages derived from Shh-dependent and -independent progenitors. We systematically characterize the gene expression programs that define these thalamic lineages across time and demonstrate how their disruption upon Shh depletion causes pronounced locomotor impairment resembling infantile Parkinson's disease. These results reveal key principles of thalamic development and provide mechanistic insights into neurodevelopmental disorders resulting from thalamic dysfunction.

Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries.

Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires the ability to forward simulate datasets from a candidate model, but because the sizes of the observed and simulated datasets usually need to match, this can be computationally expensive. Additionally, since ABC inference is based on comparisons of summary statistics computed on the observed and simulated data, using computationally expensive summary statistics can lead to further losses in efficiency. ABC has recently been applied to the family of mechanistic network models, an area that has traditionally lacked tools for inference and model choice. Mechanistic models of network growth repeatedly add nodes to a network until it reaches the size of the observed network, which may be of the order of millions of nodes. With ABC, this process can quickly become computationally prohibitive due to the resource intensive nature of network simulations and evaluation of summary statistics. We propose two methodological developments to enable the use of ABC for inference in models for large growing networks. First, to save time needed for forward simulating model realizations, we propose a procedure to extrapolate (via both least squares and Gaussian processes) summary statistics from small to large networks. Second, to reduce computation time for evaluating summary statistics, we use sample-based rather than census-based summary statistics. We show that the ABC posterior obtained through this approach, which adds two additional layers of approximation to the standard ABC, is similar to a classic ABC posterior. Although we deal with growing network models, both extrapolated summaries and sampled summaries are expected to be relevant in other ABC settings where the data are generated incrementally.

Titanium-Oxo Cluster Assisted Fabrication of a Defect-Rich Ti-MOF Membrane Showing Versatile Gas-Separation Performance.

Although having shown great promise for efficient gas separation, relevant study of Ti-MOF membranes remains very scarce, owing to limited Ti source types and uncertain factors which dominate the separation properties. In this work, we pioneered the use of the Ti8 (µ2 -O)8 (OOCC6 H5 )16 cluster as the Ti source of MIL-125 membranes, which led to lower reaction temperature and higher missing-linker number within the framework and therefore, enhanced CO2 /N2 adsorption selectivity. The MIL-125 membrane prepared by combining single-mode microwave heating with tertiary growth displayed an ideal CO2 /N2 selectivity of 38.7, which ranked the highest among all pristine pure MOF membranes measured under comparable conditions. In addition, the ideal H2 /N2 and H2 /CH4 selectivity was as high as 64.9 and 40.7, thus showing great promise for versatile utility in gas separation.

miR-590-5p targets RMND5A and promotes migration in pancreatic adenocarcinoma cell lines.

Required for meiotic nuclear division 5 homolog A (RMND5A) functions as an E3 ubiquitin ligase. To date, few studies have investigated the role of RMND5A in cancer. In the present study, the expression levels of RMND5A in multiple types of cancer were analyzed using the Gene Expression Profiling Interactive Analysis platform. The results revealed that RMND5A was highly expressed and associated with overall survival in patients with pancreatic adenocarcinoma (PAAD). A wound-healing assay revealed that RMND5A overexpression significantly increased cell migration in the PAAD cell lines AsPC-1 and PANC-1. In silico analysis predicted that RMND5A was a potential target of microRNA(miR)-590-5p. Further in vitro experiments demonstrated that overexpression of miR-590-5p downregulated the expression levels of RMND5A and decreased the migratory ability of the AsPC-1 and PANC-1 cell lines. In addition, overexpression of miR-590-5p attenuated the promoting effects of RMND5A on the migration of AsPC-1 and PANC-1 cells. The results of the present study may further elucidate the mechanisms underlying PAAD progression and provide novel targets for the treatment of PAAD.

Facile fabrication of a highly (110)-oriented ZIF-7 film with rod-shaped seeds.

In this study, we report a novel synthetic strategy to prepare a highly (110)-oriented ZIF-7 film possessing superior anti-corrosion properties via oriented epitaxial growth. Our work provides insights into facile preparation of oriented uniform MOF single seed layers and films with rod-shaped MOF seeds as building blocks.

Flexible model selection for mechanistic network models.

Network models are applied across many domains where data can be represented as a network. Two prominent paradigms for modelling networks are statistical models (probabilistic models for the observed network) and mechanistic models (models for network growth and/or evolution). Mechanistic models are better suited for incorporating domain knowledge, to study effects of interventions (such as changes to specific mechanisms) and to forward simulate, but they typically have intractable likelihoods. As such, and in a stark contrast to statistical models, there is a relative dearth of research on model selection for such models despite the otherwise large body of extant work. In this article, we propose a simulator-based procedure for mechanistic network model selection that borrows aspects from Approximate Bayesian Computation along with a means to quantify the uncertainty in the selected model. To select the most suitable network model, we consider and assess the performance of several learning algorithms, most notably the so-called Super Learner, which makes our framework less sensitive to the choice of a particular learning algorithm. Our approach takes advantage of the ease to forward simulate from mechanistic network models to circumvent their intractable likelihoods. The overall process is flexible and widely applicable. Our simulation results demonstrate the approach's ability to accurately discriminate between competing mechanistic models. Finally, we showcase our approach with a protein-protein interaction network model from the literature for yeast (Saccharomyces cerevisiae).

Manifestations of left ventricular dysfunction and arrhythmia in patients with chronic hypoparathyroidism and pseudohypoparathyroidism: a preliminary study.

BACKGROUND: Cardiac damage triggered by severe hypocalcemia is well known. However, the role of chronic hypoparathyroidism (HP) and pseudohypoparathyroidism (PHP) in cardiac health is still unclear. We investigated the effect of chronic HP and PHP on cardiac structure and conductive function in patients compiling with treatment. METHODS: The study included 18 patients with HP and eight with PHP aged 45.4 ± 15.4 and 22.1 ± 6.4 years, respectively with a previously regular follow-up. In addition, 26 age- and sex-matched healthy controls were included. General characteristics and biochemical indices were recorded. Cardiac function and structure were assessed by estimation of myocardial enzymes, B-type natriuretic peptide (BNP), and echocardiography. The 12-lead electrocardiogram and 24-h Holter electrocardiography were performed to evaluate the conductive function. RESULTS: Levels of serum calcium in HP and PHP were 2.05 ± 0.16 mmol/L and 2.25 ± 0.19 mmol/L, respectively. The levels of myocardial enzyme and BNP were within the normal range. Adjusting for age at evaluation and body mass index, all M-mode measurements, left ventricular mass (LVM), LVM index (LVMI) and relative wall thickness (RWT) were comparable between patients and controls. Prolongation of corrected QT (QTc) intervals occurred in 52.6% (10/19) of patients, and 6.7% (1/15) of patients manifested more than 100 episodes of supraventricular and ventricular extrasystoles, as well as supraventricular tachycardia. None of the above arrhythmias was related to a severe clinical event. CONCLUSIONS: From this pilot study, patients diagnosed with HP and PHP and well-controlled serum calcium levels manifested normal cardiac morphology and ventricular function, except for prolonged QTc intervals, and a small percentage of mild arrhythmias needing further investigation.

Single-cell transcriptomic analysis of adult mouse pituitary reveals sexual dimorphism and physiologic demand-induced cellular plasticity.

The anterior pituitary gland drives highly conserved physiologic processes in mammalian species. These hormonally controlled processes are central to somatic growth, pubertal transformation, fertility, lactation, and metabolism. Current cellular models of mammalian anteiror pituitary, largely built on candidate gene based immuno-histochemical and mRNA analyses, suggest that each of the seven hormones synthesized by the pituitary is produced by a specific and exclusive cell lineage. However, emerging evidence suggests more complex relationship between hormone specificity and cell plasticity. Here we have applied massively parallel single-cell RNA sequencing (scRNA-seq), in conjunction with complementary imaging-based single-cell analyses of mRNAs and proteins, to systematically map both cell-type diversity and functional state heterogeneity in adult male and female mouse pituitaries at single-cell resolution and in the context of major physiologic demands. These quantitative single-cell analyses reveal sex-specific cell-type composition under normal pituitary homeostasis, identify an array of cells associated with complex complements of hormone-enrichment, and undercover non-hormone producing interstitial and supporting cell-types. Interestingly, we also identified a Pou1f1-expressing cell population that is characterized by a unique multi-hormone gene expression profile. In response to two well-defined physiologic stresses, dynamic shifts in cellular diversity and transcriptome profiles were observed for major hormone producing and the putative multi-hormone cells. These studies reveal unanticipated cellular complexity and plasticity in adult pituitary, and provide a rich resource for further validating and expanding our molecular understanding of pituitary gene expression programs and hormone production.

Analysis in case-control sequencing association studies with different sequencing depths.

With the advent of next-generation sequencing, investigators have access to higher quality sequencing data. However, to sequence all samples in a study using next generation sequencing can still be prohibitively expensive. One potential remedy could be to combine next generation sequencing data from cases with publicly available sequencing data for controls, but there could be a systematic difference in quality of sequenced data, such as sequencing depths, between sequenced study cases and publicly available controls. We propose a regression calibration (RC)-based method and a maximum-likelihood method for conducting an association study with such a combined sample by accounting for differential sequencing errors between cases and controls. The methods allow for adjusting for covariates, such as population stratification as confounders. Both methods control type I error and have comparable power to analysis conducted using the true genotype with sufficiently high but different sequencing depths. We show that the RC method allows for analysis using naive variance estimate (closely approximates true variance in practice) and standard software under certain circumstances. We evaluate the performance of the proposed methods using simulation studies and apply our methods to a combined data set of exome sequenced acute lung injury cases and healthy controls from the 1000 Genomes project.

A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network.

Network models are applied in numerous domains where data arise from systems of interactions among pairs of actors. Both statistical and mechanistic network models are increasingly capable of capturing various dependencies among these actors. Yet, these dependencies pose statistical challenges for analyzing such data, especially when the data set comprises only a single observation of one network, often leading to intractable likelihoods regardless of the modeling paradigm and limiting the application of existing statistical methods for networks. We explore a subsampling bootstrap procedure to serve as the basis for goodness of fit and model selection with a single observed network that circumvents the intractability of such likelihoods. Our approach is based on flexible resampling distributions formed from the single observed network, allowing for more nuanced and higher dimensional comparisons than point estimates of quantities of interest. We include worked examples for model selection, with simulation, and assessment of goodness of fit, with duplication-divergence model fits for yeast (S.cerevisiae) protein-protein interaction data from the literature. The proposed approach produces a flexible resampling distribution that can be based on any network statistics of one's choosing and can be employed for both statistical and mechanistic network models.

PRIMARY HYPERPARATHYROIDISM DURING PREGNANCY: A CASE SERIES OF 8 PATIENTS.

Objective: Due to a lack of typical clinical manifestations and physiologic changes in calcium metabolism during pregnancy, primary hyperparathyroidism (PHPT) during pregnancy is commonly underdiagnosed, and treatment during this unique period presents a clinical challenge. Hence, the aim of the present study was to summarize the cases of 8 pregnant patients with PHPT who were treated at our center to provide better clinical insight into this condition. Methods: Our study comprised a retrospective analysis of 8 pregnant PHPT patients and a control group of 22 age-matched, nonpregnant PHPT patients during the same period. Clinical manifestations, biochemical indices, pathologic types, therapeutic strategies, and pregnancy outcomes were compiled, and 25 patients were screened for germline mutations in the MEN1, CDC73, and CaSR genes. Results: The most-common symptoms in the pregnancy group involved the gastrointestinal tract (GIT) in 7/8 cases (87.5%), followed by urinary system involvement (50%) and joint pain (50%). In contrast, GIT symptoms in the control group were significantly less common (31.82%; P = .012). There was a trend of more-severe elevation of serum parathyroid hormone levels in the control group compared to that in the pregnancy group (P = .053). No differences were found in blood-ionized calcium, phosphate, or alkaline phosphatase levels between the two groups. In the pregnancy group, the serum albumin-corrected calcium level was reduced from 3.42 ± 0.66 mmol/L to 2.89 ± 0.46 mmol/L (P = .025) after hydration and medical treatment. Six patients, three of whom were in the second trimester of pregnancy, underwent parathyroidectomy, and 3 patients were after childbirth or had induced labor. Postoperative serum calcium levels were reduced to within the normal range. Fetal/neonatal complications were observed in 4 of 5 patients who had not received surgical treatment during pregnancy. In addition, 2 of 5 pregnant PHPT patients were found to carry MEN1 mutations, whereas no mutations were detected in any of the 20 nonpregnant patients. Conclusion: In this case series of PHPT during pregnancy, the most-common complaint of GIT symptoms may be easily confused with pregnancy reactions, which might contribute to the under- or misdiagnosis of this clinical entity. Patients who did not receive surgical treatment during pregnancy had high incidences of fetal/neonatal complications and worse pregnancy outcomes. Abbreviations: CaSR = calcium-sensing receptor; CDC73 = cell division cycle 73; GIT = gastrointestinal tract; MEN = multiple endocrine neoplasia; PHPT = primary hyperparathyroidism; PTH = parathyroid hormone; SCa = serum calcium.

ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.

Efficient vaccination strategies for epidemic control using network information.

BACKGROUND: Network-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at individual and village levels affected network-based vaccination effectiveness. METHODS: We simulated a Susceptible-Infected-Recovered process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level network properties for simulated datasets from 10 representative villages. Second, we simulated vaccinating 10% of each of the 75 empirical village networks at baseline, selecting vaccinees through one of five network-based approaches: random individuals (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree individuals (Highest Degree); or most central individuals (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces the data collection burden but generates only partially observed networks. RESULTS: In regression analysis, we found mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, the Nomination method reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. The High Degree approach had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals' degrees at three was as effective as using complete networks. CONCLUSIONS: Using even partial network information to prioritize vaccines at either the village or individual level, i.e. determine the optimal order of communities or individuals within each village, substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required.