Loss-of-function mutation in PRMT9 causes abnormal synapse development by dysregulation of RNA alternative splicing.

Protein arginine methyltransferase 9 (PRMT9) is a recently identified member of the PRMT family, yet its biological function remains largely unknown. Here, by characterizing an intellectual disability associated PRMT9 mutation (G189R) and establishing a Prmt9 conditional knockout (cKO) mouse model, we uncover an important function of PRMT9 in neuronal development. The G189R mutation abolishes PRMT9 methyltransferase activity and reduces its protein stability. Knockout of Prmt9 in hippocampal neurons causes alternative splicing of ~1900 genes, which likely accounts for the aberrant synapse development and impaired learning and memory in the Prmt9 cKO mice. Mechanistically, we discover a methylation-sensitive protein-RNA interaction between the arginine 508 (R508) of the splicing factor 3B subunit 2 (SF3B2), the site that is exclusively methylated by PRMT9, and the pre-mRNA anchoring site, a cis-regulatory element that is critical for RNA splicing. Additionally, using human and mouse cell lines, as well as an SF3B2 arginine methylation-deficient mouse model, we provide strong evidence that SF3B2 is the primary methylation substrate of PRMT9, thus highlighting the conserved function of the PRMT9/SF3B2 axis in regulating pre-mRNA splicing.

SIRT1 coordinates transcriptional regulation of neural activity and modulates depression-like behaviors in the nucleus accumbens.

BACKGROUND: Major depression and anxiety disorder are significant causes of disability and socio-economic burden. Despite the prevalence and considerable impact of these affective disorders, their pathophysiology remains elusive. Thus, there is an urgent need to develop novel therapeutics for these conditions. We evaluated the role of SIRT1 in regulating dysfunctional processes of reward by using chronic social defeat stress (CSDS) to induce depression- and anxiety-like behaviors. CSDS induces physiological and behavioral changes that recapitulate depression-like symptomatology and alters gene expression programs in the nucleus accumbens, yet cell type-specific changes in this critical structure remain largely unknown. METHODS: We examined transcriptional profiles of D1-MSNs lacking deacetylase activity of SIRT1 by RNA sequencing (RNA-Seq) in a cell-type specific manner using the RiboTag line of mice. We analyzed differentially expressed genes using gene ontology tools including SynGO and EnrichR, and further demonstrated functional changes in D1-MSN specific SIRT1-KO mice using electrophysiological and behavioral measurements. RESULTS: RNAseq revealed altered transcriptional profiles of D1-MSNs lacking functional SIRT1 and showed specific changes in synaptic genes including glutamatergic and GABAergic receptors in D1-MSNs. These molecular changes may be associated with decreased excitatory and increased inhibitory neural activity in Sirt1-KO D1-MSNs, accompanied by morphological changes. Moreover, the D1-MSN-specific Sirt1-KO mice exhibited pro-resilient changes in anxiety- and depression-like behaviors. CONCLUSIONS: SIRT1 coordinates excitatory and inhibitory synaptic genes to regulate GABAergic output tone of D1-MSNs. These findings reveal a novel signaling pathway that has the potential for the development of innovative treatments for affective disorders.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Recent advances in spatially variable gene detection in spatial transcriptomics.

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Placebo and Nocebo Responses in Pharmacological Trials of Tic Disorders: A Meta-Analysis.

BACKGROUND: Clinical trials of new drugs for tic disorders (TD) often fail to yield positive results. Placebo and nocebo responses play a vital role in interpreting the outcomes of randomized controlled trials (RCTs), yet these responses in RCTs of TD remain unexplored. OBJECTIVE: The aim was to assess the magnitude of placebo and nocebo responses in RCTs of pharmacological interventions for TD and identify influencing factors. METHODS: A systematic search of the Embase, Medline, Cochrane Central Register of Controlled Trials, and PsycINFO databases was conducted. Eligible studies were RCTs that compared active pharmacological agents with placebos. Placebo response was defined as the change from baseline in TD symptom severity in the placebo group, and nocebo response as the proportion experiencing adverse events (AEs) in this group. Subgroup analysis and meta-regression were performed to explore modifying factors. RESULTS: Twenty-four trials involving 2222 participants were included in this study. A substantial placebo response in TD symptom severity was identified, with a pooled effect size of -0.79 (95% confidence interval [CI] -0.99 to -0.59; I2 = 67%). Forty-four percent (95% CI 27% to 63%; I2 = 92%) of patients experienced AEs while taking inert pills. Sample size, study design, and randomization ratio were correlated with changes in placebo and nocebo responses. CONCLUSION: There were considerable placebo and nocebo responses in TD clinical trials. These results are of great relevance for the design of future trials and for clinical practice in TD. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration ID CRD42023388397. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

BACKGROUND: Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. METHODS: We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. RESULTS: wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. CONCLUSIONS: The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS.

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases.

GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power, or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT( https://github.com/wangjr03/BayesKAT ), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules, and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Disrupted Maturation of Prefrontal Layer 5 Neuronal Circuits in an Alzheimer's Mouse Model of Amyloid Deposition.

Mutations in genes encoding amyloid precursor protein (APP) and presenilins (PSs) cause familial forms of Alzheimer's disease (AD), a neurodegenerative disorder strongly associated with aging. It is currently unknown whether and how AD risks affect early brain development, and to what extent subtle synaptic pathology may occur prior to overt hallmark AD pathology. Transgenic mutant APP/PS1 over-expression mouse lines are key tools for studying the molecular mechanisms of AD pathogenesis. Among these lines, the 5XFAD mice rapidly develop key features of AD pathology and have proven utility in studying amyloid plaque formation and amyloid ß (Aß)-induced neurodegeneration. We reasoned that transgenic mutant APP/PS1 over-expression in 5XFAD mice may lead to neurodevelopmental defects in early cortical neurons, and performed detailed synaptic physiological characterization of layer 5 (L5) neurons from the prefrontal cortex (PFC) of 5XFAD and wild-type littermate controls. L5 PFC neurons from 5XFAD mice show early APP/Aß immunolabeling. Whole-cell patch-clamp recording at an early post-weaning age (P22-30) revealed functional impairments; although 5XFAD PFC-L5 neurons exhibited similar membrane properties, they were intrinsically less excitable. In addition, these neurons received smaller amplitude and frequency of miniature excitatory synaptic inputs. These functional disturbances were further corroborated by decreased dendritic spine density and spine head volumes that indicated impaired synapse maturation. Slice biotinylation followed by Western blot analysis of PFC-L5 tissue revealed that 5XFAD mice showed reduced synaptic AMPA receptor subunit GluA1 and decreased synaptic NMDA receptor subunit GluN2A. Consistent with this, patch-clamp recording of the evoked L23>L5 synaptic responses revealed a reduced AMPA/NMDA receptor current ratio, and an increased level of AMPAR-lacking silent synapses. These results suggest that transgenic mutant forms of APP/PS1 overexpression in 5XFAD mice leads to early developmental defects of cortical circuits, which could contribute to the age-dependent synaptic pathology and neurodegeneration later in life.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning.

Hepatocellular carcinoma (HCC) is a leading malignant liver tumor with high mortality and morbidity. Patients at the same stage can be defined as different molecular subtypes associated with specific genomic disorders and clinical features. Thus, identifying subtypes is essential to realize efficient treatment and improve survival outcomes of HCC patients. Here, we applied a regularized multiple kernel learning with locality preserving projections method to integrate mRNA, miRNA and DNA methylation data of HCC patients to identify subtypes. We identified two HCC subtypes significantly correlated with the overall survival. The patient 3-years mortality rates in the high-risk and low-risk group was 51.0% and 23.5%, respectively. The high-risk group HCC patients were 3.37 times higher in death risk compared to the low-risk group after adjusting for clinically relevant covariates. A total of 196 differentially expressed mRNAs, 2,151 differentially methylated genes and 58 differentially expressed miRNAs were identified between the two subtypes. Additionally, pathway activity analysis showed that the activities of six pathways between the two subtypes were significantly different. Immune cell infiltration analysis revealed that the abundance of nine immune cells differed significantly between the two subtypes. We further applied the weighted gene co-expression network analysis to identify gene modules that may affect patients prognosis. Among the identified modules, the key module genes significantly associated with prognosis were found to be involved in multiple biological processes and pathways, revealing the mechanism underlying the progression of HCC. Hub gene analysis showed that the expression levels of CDK1, CDCA8, TACC3, and NCAPG were significantly associated with HCC prognosis. Our findings may bring novel insights into the subtypes of HCC and promote the realization of precision medicine.

Early impairment of cortical circuit plasticity and connectivity in the 5XFAD Alzheimer's disease mouse model.

Genetic risk factors for neurodegenerative disorders, such as Alzheimer's disease (AD), are expressed throughout the life span. How these risk factors affect early brain development and function remain largely unclear. Analysis of animal models with high constructive validity for AD, such as the 5xFAD mouse model, may provide insights on potential early neurodevelopmental effects that impinge on adult brain function and age-dependent degeneration. The 5XFAD mouse model over-expresses human amyloid precursor protein (APP) and presenilin 1 (PS1) harboring five familial AD mutations. It is unclear how the expression of these mutant proteins affects early developing brain circuits. We found that the prefrontal cortex (PFC) layer 5 (L5) neurons in 5XFAD mice exhibit transgenic APP overloading at an early post-weaning age. Impaired synaptic plasticity (long-term potentiation, LTP) was seen at 6-8 weeks age in L5 PFC circuit, which was correlated with increased intracellular APP. APP overloading was also seen in L5 pyramidal neurons in the primary visual cortex (V1) during the critical period of plasticity (4-5 weeks age). Whole-cell patch clamp recording in V1 brain slices revealed reduced intrinsic excitability of L5 neurons in 5XFAD mice, along with decreased spontaneous miniature excitatory and inhibitory inputs. Functional circuit mapping using laser scanning photostimulation (LSPS) combined with glutamate uncaging uncovered reduced excitatory synaptic connectivity onto L5 neurons in V1, and a more pronounced reduction in inhibitory connectivity, indicative of altered excitation and inhibition during VC critical period. Lastly, in vivo single-unit recording in V1 confirmed that monocular visual deprivation-induced ocular dominance plasticity during critical period was impaired in 5XFAD mice. Our study reveals plasticity deficits across multiple cortical regions and indicates altered early cortical circuit developmental trajectory as a result of mutant APP/PS1 over-expression.

Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: A joint similarity network fusion approach.

Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.

Reduced HGF/MET Signaling May Contribute to the Synaptic Pathology in an Alzheimer's Disease Mouse Model.

Alzheimer's disease (AD) is a neurodegenerative disorder strongly associates with aging. While amyloid plagues and neurofibrillary tangles are pathological hallmarks of AD, recent evidence suggests synaptic dysfunction and physical loss may be the key mechanisms that determine the clinical syndrome and dementia onset. Currently, no effective therapy prevents neuropathological changes and cognitive decline. Neurotrophic factors and their receptors represent novel therapeutic targets to treat AD and dementia. Recent clinical literature revealed that MET receptor tyrosine kinase protein is reduced in AD patient's brain. Activation of MET by its ligand hepatocyte growth factor (HGF) initiates pleiotropic signaling in the developing brain that promotes neurogenesis, survival, synaptogenesis, and plasticity. We hypothesize that if reduced MET signaling plays a role in AD pathogenesis, this might be reflected in the AD mouse models and as such provides opportunities for mechanistic studies on the role of HGF/MET in AD. Examining the 5XFAD mouse model revealed that MET protein exhibits age-dependent progressive reduction prior to overt neuronal pathology, which cannot be explained by indiscriminate loss of total synaptic proteins. In addition, genetic ablation of MET protein in cortical excitatory neurons exacerbates amyloid-related neuropathology in 5XFAD mice. We further found that HGF enhances prefrontal layer 5 neuron synaptic plasticity measured by long-term potentiation (LTP). However, the degree of LTP enhancement is significantly reduced in 5XFAD mice brain slices. Taken together, our study revealed that early reduction of HGF/MET signaling may contribute to the synaptic pathology observed in AD.

Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal traits.

Correlated phenotypes often share common genetic determinants. Thus, a multi-trait analysis can potentially increase association power and help in understanding pleiotropic effect. When multiple traits are jointly measured over time, the correlation information between multivariate longitudinal responses can help to gain power in association analysis, and the longitudinal traits can provide insights on the dynamic gene effect over time. In this work, we propose a multivariate partially linear varying coefficients model to identify genetic variants with their effects potentially modified by environmental factors. We derive a testing framework to jointly test the association of genetic factors and illustrated with a bivariate phenotypic trait, while taking the time varying genetic effects into account. We extend the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficient functions. Theoretical results such as consistency and asymptotic normality of the estimates are established. The performance of the testing procedure is evaluated through Monte Carlo simulation studies. The utility of the method is demonstrated with a real data set from the Twin Study of Hormones and Behavior across the menstrual cycle project, in which single nucleotide polymorphisms associated with emotional eating behavior are identified.

Adverse childhood experiences in depression and the mediating role of multimorbidity in mid-late life: A nationwide longitudinal study.

BACKGROUNDS: Adverse childhood experiences are co-occurring factors of multimorbidity and depression in mid-late life, but the combined effect of ACEs and multimorbidity on depression over life has not been fully studied. METHODS: We used data from the China Health and Retirement Longitudinal Study which includes 4,440 middle-aged and older adults. Different types of ACEs experienced up to the age of 17 were assessed based on self-reports. We used parallel process Latent Growth Curve modelling to evaluate the longitudinal mediation role of ACEs, multimorbidity and depression. RESULTS: People who had more ACEs were found to have a higher level of multimorbidity (intercept: 0.057, 95% CI: 0.031 to 0.079) and depression (intercept: 0.047, 95% CI: 0.013 to 0.076) at the baseline and a faster increase in multimorbidity (slope: 0.107, 95%CI: 0.078 to 0.136) and depression (slope: 0.074, 95%CI: 0.035 to 0.153). The mediation analysis indicated that there was a positive indirect association of ACEs via the multimorbidity intercept with the intercept of depression (0.028, 95%CI: 0.012 to 0.043), and a small negative association with the slope of depression (-0.002, 95%CI: -0.003 to -0.001). We also found a positive indirect association of ACEs via the multimorbidity slope with the intercept (0.035, 95%CI: 0.021 to 0.049) and slope (0.008, 95%CI: 0.004 to 0.011) of depression. CONCLUSIONS: ACEs were related to higher depression partly via elevated multimorbidity. Public health services and behavioural interventions to prevent and reduce the occurrence of ACEs might help to lower the risk of multimorbidity and depression in later life.

Gene set analysis with graph-embedded kernel association test.

MOTIVATION: Kernel-based association test (KAT) has been a popular approach to evaluate the association of expressions of a gene set (e.g. pathway) with a phenotypic trait. KATs rely on kernel functions which capture the sample similarity across multiple features, to capture potential linear or non-linear relationship among features in a gene set. When calculating the kernel functions, no network graphical information about the features is considered. While genes in a functional group (e.g. a pathway) are not independent in general due to regulatory interactions, incorporating regulatory network (or graph) information can potentially increase the power of KAT. In this work, we propose a graph-embedded kernel association test, termed gKAT. gKAT incorporates prior pathway knowledge when constructing a kernel function into hypothesis testing. RESULTS: We apply a diffusion kernel to capture any graph structures in a gene set, then incorporate such information to build a kernel function for further association test. We illustrate the geometric meaning of the approach. Through extensive simulation studies, we show that the proposed gKAT algorithm can improve testing power compared to the one without considering graph structures. Application to a real dataset further demonstrate the utility of the method. AVAILABILITY AND IMPLEMENTATION: The R code used for the analysis can be accessed at https://github.com/JialinQu/gKAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of an alternative medium via completely replaces the medium components by mixed wastewater and crude glycerol for efficient production of docosahexaenoic acid by Schizochytrium sp.

Exorbitant substrates for Schizochytrium culture result in the high cost of docosahexaenoic acid (DHA) production. In order to develop a feasible approach that is expected to reduce DHA production cost, Schizochytrium sp. S31 cultivation with a mixture of saline wastewater (SWW) and tofu whey wastewater (TWW) was investigated in this study. Using glucose as the carbon source, the maximum biomass and DHA yield in cultures using mixed wastewater containing 5% SWW reached 19.08 and 2.66 g/L, respectively, which were 2.29 and 2.66 times higher than those of cultures using control medium. Moreover, a good wastewater treatment performance was achieved as approximately 60% of the COD, TN, and TP were reduced in the cultures using mixed wastewater with a SWW ratio of 5%. The mixed wastewater presented better performance on DHA production than control medium using all tested carbon sources including glucose, fructose, and pure and crude glycerol. The components of control medium can be completely replaced by the mixed wastewater and crude glycerol. It is expected to effectively decrease the medium cost for DHA production and reduce the environmental risk of food processing wastewater.

A conditional autoregressive model for genetic association analysis accounting for genetic heterogeneity.

Converging evidence from genetic studies and population genetics theory suggest that complex diseases are characterized by remarkable genetic heterogeneity, and individual rare mutations with different effects could collectively play an important role in human diseases. Many existing statistical models for association analysis assume homogeneous effects of genetic variants across all individuals, and could be subject to power loss in the presence of genetic heterogeneity. To consider possible heterogeneous genetic effects among individuals, we propose a conditional autoregressive model. In the proposed method, the genetic effect is considered as a random effect and a score test is developed to test the variance component of genetic random effect. Through simulations, we compare the type I error and power performance of the proposed method with those of the generalized genetic random field and the sequence kernel association test methods under different disease scenarios. We find that our method outperforms the other two methods when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals or subgroups of individuals. Finally, we illustrate the new method by applying it to the whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

Disrupted Timing of MET Signaling Derails the Developmental Maturation of Cortical Circuits and Leads to Altered Behavior in Mice.

The molecular regulation of the temporal dynamics of circuit maturation is a key contributor to the emergence of normal structure-function relations. Developmental control of cortical MET receptor tyrosine kinase, expressed early postnatally in subpopulations of excitatory neurons, has a pronounced impact on the timing of glutamatergic synapse maturation and critical period plasticity. Here, we show that using a controllable overexpression (cto-Met) transgenic mouse, extending the duration of MET signaling after endogenous Met is switched off leads to altered molecular constitution of synaptic proteins, persistent activation of small GTPases Cdc42 and Rac1, and sustained inhibitory phosphorylation of cofilin. These molecular changes are accompanied by an increase in the density of immature dendritic spines, impaired cortical circuit maturation of prefrontal cortex layer 5 projection neurons, and altered laminar excitatory connectivity. Two photon in vivo imaging of dendritic spines reveals that cto-Met enhances de novo spine formation while inhibiting spine elimination. Extending MET signaling for two weeks in developing cortical circuits leads to pronounced repetitive activity and impaired social interactions in adult mice. Collectively, our data revealed that temporally controlled MET signaling as a critical mechanism for controlling cortical circuit development and emergence of normal behavior.