Biology subjects

He, M.

Publications and source records attributed to He, M..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

Effect of Total Sphingomyelin Synthase Activity on Low Density Lipoprotein Catabolism in Mice

BackgroundSphingomyelin (SM) and cholesterol are two key lipid partners on cell membranes and on lipoproteins. Many studies have indicated the influence of cholesterol on SM metabolism. This study examined the influence of SM biosynthesis on cholesterol metabolism. MethodsInducible global Sms1 KO/global Sms2 KO mice were prepared to evaluate the effect of whole-body SM biosynthesis deficiency on lipoprotein metabolism. Tissue cholesterol, SM, ceramide, and glucosylceramide levels were measured. TG production rate and LDL catabolism were measured. Lipid rafts were isolated and LDL receptor mass and function were evaluated. Also, the effects of exogenous sphingolipids on hepatocytes were investigated. ResultsWe found that total SMS depletion significantly reduced plasma SM levels. Also, the total deficiency significantly induced plasma cholesterol, apoB, and apoE levels. Importantly, total SMS deficiency, but not SMS2 deficiency, dramatically decreased LDL receptors in the liver and attenuated LDL uptake through the receptor. Further, we found that total SMS deficiency greatly reduced LDL receptors in the lipid rafts which contained significantly lower SM and significantly higher glucosylceramide as well as cholesterol. Furthermore, we treated primary hepatocytes and Huh7 cells (a human hepatoma cell line) with SM, ceramide, or glucosylceramide, and we found that only SM could up-regulate LDL receptor levels in a dose-dependent fashion. ConclusionsWhole-body SM biosynthesis plays an important role in LDL-cholesterol catabolism. The total SMS deficiency, but not SMS2 deficiency, reduces LDL uptake and causes LDL-cholesterol accumulation in the circulation. Given the fact that serum SM level is a risk factor for cardiovascular diseases, inhibiting SMS2 but not SMS1 should be the desirable approach. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/527088v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@50757eorg.highwire.dtl.DTLVardef@51ad9dorg.highwire.dtl.DTLVardef@5d362corg.highwire.dtl.DTLVardef@18fbe63_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Adipocyte lipin 1 is positively associated with metabolic health in humans and regulates systemic metabolism in mice.

Dysfunctional adipose tissue is believed to promote the development of hepatic steatosis and systemic insulin resistance, but many of the mechanisms involved are still unclear. Lipin 1 catalyzes the conversion of phosphatidic acid to diacylglycerol (DAG), the penultimate step of triglyceride synthesis, which is essential for lipid storage. Herein we found that adipose tissue LPIN1 expression is decreased in people with obesity compared to lean subjects and low LPIN1 expression correlated with multi-tissue insulin resistance and increased rates of hepatic de novo lipogenesis. Comprehensive metabolic and multi-omic phenotyping demonstrated that adipocyte-specific Lpin1-/- mice had a metabolically-unhealthy phenotype, including liver and skeletal muscle insulin resistance, hepatic steatosis, increased hepatic de novo lipogenesis, and transcriptomic signatures of nonalcoholic steatohepatitis that was exacerbated by high-fat diets. We conclude that adipocyte lipin 1-mediated lipid storage is vital for preserving adipose tissue and systemic metabolic health and its loss predisposes mice to nonalcoholic steatohepatitis.

physiology↗

bioRxiv2022

MYSM1 co-activates ERα action via histone and non-histone deubiquitination to confer antiestrogen resistance in breast cancer

Endocrine resistance is a crucial challenge in estrogen receptor alpha (ER)-positive breast cancer (BCa) therapy. Aberrant alteration in modulation of E2/ER signaling pathway has emerged as the putative contributor for endocrine resistance in BCa. Thus, identification the efficient ER cofactor remains necessary for finding a potential therapeutic target for endocrine resistance. Herein, we have demonstrated that Myb like, SWIRM and MPN domains 1 (MYSM1) as a histone deubiquitinase is a novel ER co-activator with established Drosophila experimental model. Our results showed that MYSM1 participated in up-regulation of ER action via histone and non-histone deubiquitination. We provided the evidence to show that MYSM1 was involved in maintenance of ER stability via ER deubiquitination. Furthermore, silencing MYSM1 induced enhancement of histone H2A ubiquitination as well as reduction of histone H3K4me3 and H3Ac levels at cis regulatory elements on promoter of ER-regulated gene. In addition, MYSM1 depletion attenuated cell proliferation/growth in BCa-derived cell lines and xenograft models. Knockdown of MYSM1 increased the sensitivity of antiestrogen agents in BCa cells. MYSM1 was highly expressed in clinical BCa samples, especially in aromatase inhibitor (AI) non-responsive tissues. These findings clarify the molecular mechanism of MYSM1 as an epigenetic modifier in regulation of ER action and provide a potential therapeutic target for endocrine resistance in BCa.

cell biology↗

bioRxiv2022

Age-related decline in prefrontal glutamate predicts failure to efficiently deploy working memory in the service of learning

The ability to use past experience to effectively guide decision making declines in older adulthood. Such declines have been theorized to emerge from either impairments of striatal reinforcement learning systems (RL) or impairments of recurrent networks in prefrontal and parietal cortex that support working memory (WM). Distinguishing between these hypotheses has been challenging because either RL or WM could be used to facilitate successful decision making in typical laboratory tasks. Here we investigated the neurocomputational correlates of age-related decision making deficits using an RL-WM task to disentangle these mechanisms, a computational model to quantify them, and magnetic resonance spectroscopy to link them to their molecular bases. Our results reveal that task performance is worse in older age, in a manner best explained by working memory deficits, as might be expected if cortical recurrent networks were unable to sustain persistent activity across multiple trials. Consistent with this, we show that older adults had lower levels of prefrontal glutamate, the excitatory neurotransmitter thought to support persistent activity, compared to younger adults. Individuals with the lowest prefrontal glutamate levels displayed the greatest impairments in working memory after controlling for other anatomical and metabolic factors. Together, our results suggest that lower levels of prefrontal glutamate may contribute to failures of working memory systems and impaired decision making in older adulthood.

animal behavior and cognition↗

bioRxiv2022

Switching state-space modeling of neural signal dynamics

Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state-inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner. Author summaryAn inherent aspect of brain activity is that it changes over time, but existing methods for analyzing neuroscience data typically assume that the underlying activity is strictly stationary, i.e., the properties of that activity do not change over time. One way of handling time-varying data is to break the data into smaller segments that one assumes to be quasi-stationary, but this approach only works if signals vary gradually, and tends to perform poorly when changes are rapid or the target activity is transient in nature. A class of models called linear switching state-space models can explicitly represent time-varying activity, but they pose another set of challenges: exact solutions for such models are intractable, and existing approximate solutions can be highly inaccurate. In this work we present a solution for linear switching state-space models that is able to recover the underlying hidden states and model parameters for time-varying dynamics in a way that is robust to model mis-specification and that outperforms previously proposed methods. We demonstrate the utility of our method by applying it to the problem of sleep spindle detection and show that switching state-space models can automatically detect transient spindle activity from human sleep electroencephalograms.

neuroscience↗

bioRxiv2022

An iterative search algorithm to identify oscillatory dynamics in neurophysiological time series

Neural oscillations have long been recognized for their mechanistic importance in coordinating activity within and between brain circuits. Co-occurring broad-band, non-periodic signals are also ubiquitous in neural data and are thought to reflect the characteristics of populationlevel neuronal spiking activity. Identifying oscillatory activity distinct from broadband signals is therefore an important, yet surprisingly difficult, problem in neuroscience. Commonly-used bandpass filters produce spurious oscillations when applied to broad-band noise and may be illinformed by canonical frequency bands. Curve-fitting procedures have been developed to identify peaks in the power spectrum distinct from broadband noise. Unfortunately, these ad hoc methods are prone to overfitting and are difficult to interpret in the absence of generative models to formally represent oscillatory behavior. Here we present a novel method to identify and characterize neural oscillations distinct from broad-band noise. First, we propose a new conceptual construct that makes clear, from a dynamical systems perspective, when oscillations are present or not. We then use this construct to develop generative models for neural oscillations. We show through extensive analyses of simulated and human EEG data that our approach identifies oscillations and their characteristics far more accurately than widely used methods, achieving near-perfect recovery of the number of oscillations when the SNR exceeds a very modest threshold. We also show that our method can automatically identify subject-level variations in frequency to overcome the limitations of fixed canonical frequency bands. Finally we demonstrate how our method can extract clinically-relevant neurophysiological features with greater statistical efficiency other established methods.

neuroscience↗

bioRxiv2022

Expansion Spatial Transcriptomics

Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in diverse tissue contexts, however, their spatial resolution is limited by array density. We present Expansion Spatial Transcriptomics (Ex-ST) to overcome this limitation by clearing and expanding tissue prior to capturing the entire transcriptome. This approach allows us to achieve near cellular resolution and higher capture efficiency of lowly-expressed genes, which we demonstrate using mouse brain samples.

genomics↗

bioRxiv2022

Searching across-cohort relatives via encrypted genotype regression

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author SummaryEstimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated.

genetics↗

bioRxiv2022

Network-based hub biomarker discovery for glaucoma

Glaucoma is an optic neuropathy, and the leading cause of irreversible blindness worldwide. However, the early detection of glaucoma remains challenging as chronic forms of glaucoma remain largely asymptomatic until considerable irreversible visual field deficits have ensued. Thus, biomarkers that facilitate early diagnosis and treatment for patients with a high risk of progression are critical. Network medicine approaches can be useful in identifying key relationships and important biomolecules for complex diseases. In this paper, we identified several hub biomarkers/drug targets for the diagnosis, treatment and prognosis for glaucoma and explored their associations for glaucoma based on human disease-biomarker and disease-target-drug networks. These results were verified by text-mining and genomic/epidemiology data. We also predicted the new application of BMP1 and MMP9 to diagnose glaucoma and confirm the theory of hub biomarkers with multiple clinical applications. Further, relevant pivotal pathways (regulation of the multicellular organismal process, regulation of localisation, and cytoplasmic vesicle for biomarkers; signal transduction and developmental process for targets) for these hub biomolecules were discovered, which may be foundations for future biomarker and drug target prediction for glaucoma. In conclusion, based on complex networks, hub biomolecules, essential pathways, and close diseases were identified for glaucoma in diagnosis, treatment and prognosis.

bioinformatics↗

bioRxiv2022

An uncertainty-based interpretable deep learning framework for breast cancer outcomes prediction

Accurate prediction of breast cancer outcomes is important for selecting appropriate treatment, which can prolong the survival period of the patients and improve the life quality. Recently, different deep learning-based methods are carefully designed for cancer outcomes prediction. However, the applications of these methods are still challenging due to the model interpretability. In this study, we proposed a novel multi-task deep neural network UISNet to interpret the feature importance of the prediction model by an uncertainty-based integrated gradients algorithm. Additionally, UISNet improves the prediction accuracy by introducing the prior biological pathway knowledge and utilizing the patients heterogeneity information. By applications to seven breast cancer public datasets, the method was shown to outperform state-of-the-art methods by achieving a 5.79% higher C-index value on average. For the identified genes based on the interpretable model, 11 out of the top 20 genes have been proved to be associated with breast cancer by literature review. The comprehensive tests indicated that our proposed method is accurate and robust to predict breast cancer outcomes, and is an effective way to identify the prognosis-related genes. The method codes are available at: https://github.com/chh171/UISNet.

bioinformatics↗

bioRxiv2022

Translation control by maternal Nanog promotes oocyte maturation and early embryonic development

Many maternal mRNAs are translationally repressed during oocyte maturation and spatio-temporally activated during early embryogenesis, which is critical for oocyte and early embryo development. By analyzing maternal mutants of nanog (Mnanog) in zebrafish, we demonstrated that Nanog tightly controls translation of maternal mRNA during oocyte maturation via transcriptional repression of eukaryotic translation elongation factor 1 alpha 1, like 2 (eef1a1l2). Loss of maternal Nanog led to defects of egg maturation, increased endoplasmic reticulum (ER) stress, and an activated unfold protein response (UPR), which was caused by elevated translational activity. We further demonstrated that Nanog, as a transcriptional repressor, represses the transcription of eefl1a1l2 by directly binding to the eef1a1l2 promoter during oocyte maturation. More importantly, depletion of eef1a1l2 in nanog mutant females effectively rescued the elevated translational activity in oocytes, egg quality defects, and embryonic defects of Mnanog embryos. Thus, our study demonstrates that maternal Nanog regulates oocyte maturation and early embryogenesis though translational control of maternal mRNA via a novel mechanism, in which Nanog acts as a transcriptional repressor to suppress transcription of eef1a1l2.

developmental biology↗

bioRxiv2022

Dnmt3bas regulates transcriptional induction and alternative splicing of Dnmt3b

During mammalian embryogenesis, DNMT3B activity is critical for the genome-wide establishment of DNA methylation. Using naive ESC differentiation as a model, we elucidated the mechanism by which lncRNA, Dnmt3bas, controls the inducible expression and alternative splicing of Dnmt3b. Our data showed that Dnmt3bas knockdown increased transcriptional induction and decreased H3K27me3 at Dnmt3b cis-regulatory elements post-differentiation. Notably, transcriptional induction of Dnmt3b was accompanied by exon inclusion, switching the major isoform from catalytically inactive Dnmt3b6 to the active Dnmt3b1. While Dnmt3bas overexpression attenuated Dnmt3b induction, it increased the Dnmt3b1:Dnmt3b6 ratio. This observation was explained by a specific interaction of Dnmt3bas with hnRNPL, which promotes exon inclusion. These data suggest that Dnmt3bas coordinates alternative splicing and transcriptional induction of Dnmt3b by facilitating the interaction of hnRNPL and RNA Pol II at the Dnmt3b promoter. This two-pronged mechanism would tightly control DNMT3B activity, ensuring the fidelity and specificity of de novo DNA methylation during development.

molecular biology↗

bioRxiv2022

Distinct Single-cell Immune Ecosystems Distinguish True and De Novo HBV-related Hepatocellular Carcinoma Recurrences

Revealing differential tumor immune microenvironment (TIME) characteristics between true versus de novo hepatocellular carcinoma (HCC) recurrence could help optimal development and use of immunotherapies. Here, we studied the TIME of recurrent HBV-related HCCs by 5and VDJ single-cell and bulk RNA-sequencing, flow cytometry, and multiplexed immunofluorescence. Analyses of mutational profiles, evolutionary trajectories, and clonal architecture using whole-exome sequencing identified de novo versus true recurrences, some of which occurred before clinical diagnosis. The TIME of truly recurrent HCCs was characterized by an increased abundance in KLRB1+CD8+ T cells with memory phenotype and low cytotoxicity. In contrast, we found an enrichment in cytotoxic and exhausted CD8+ T cells in the TIME of de novo recurrent HCCs. Transcriptomic and interaction analyses showed an upregulated GDF15 expression level on HCC cells in proximity to dendritic cells, which may have dampened antigen presentation and inhibited anti-tumor immunity in the TIME of truly recurrent lesions. In contrast, we found that myeloid cells crosstalk with T cells mediated T cell exhaustion and immunosuppression in the TIME of de novo recurrent HCC. In conclusion, our results support genomic diagnosis and immune profiling for guiding immunotherapy implementation based on the type of HCC recurrence and TIME. HighlightsO_LITruly recurrent lesions are seeded before primary tumor diagnosis, and that de novo cancer can occur earlier than the clinically used 2-year limit. C_LIO_LIScRNA-seq unravels distinct immune ecosystems in true versus de novo HCC recurrences, highlighting the need for different immunotherapy strategies for two types of HCC recurrence. C_LIO_LICD8+ T cells in de novo recurrence displayed cytotoxic and exhausted phenotypes while those in truly recurrent lesions showed a memory phenotype with weak cytotoxicity. C_LIO_LIHCC cells expressing the inhibitory molecule GDF15 were in the proximity of DCs only in truly recurrent lesions. C_LIO_LIHigh GDF15 expression level was associated with truly recurrent HCC and worse prognosis. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/494526v1_ufig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@1b76bf0org.highwire.dtl.DTLVardef@10f889aorg.highwire.dtl.DTLVardef@d16b52org.highwire.dtl.DTLVardef@dceb7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Epitranscriptomic Modification of MicroRNA Increases Atherosclerosis Susceptibility

Emerging evidence indicates that oxidative stress causes the hydroxylation of guanine (G) to generate 8-oxo-7,8-dihydro guanosine (8OH-G) in microRNAs (miRs), which induces the guanine-to-uracil (G-to-U) transversion and thus changes the miR targetomes. However, whether and how the 8OH-G-modified miRs are involved in vascular endothelial dysfunction and atherogenesis were unexplored. Using 8OH-G crosslinking immunoprecipitation miR sequencing (8OH-G CLIP-miR-seq), we found that 8OH-G miR-483 were among the most enriched 8OH-G miR species in ECs induced by ox-LDL. Transcriptomic profiling by RNA-seq indicated that the G-to-U transversion of miR-483 altered the original mRNA targeting efficacy and allows 8OH-G miR-483 to recognize new mRNA target sites. A reduced ratio of 8OH-G miR-483 to miR-483 in lung ECs was found in the endothelial-specific miR-483 transgenic (EC-miR-483 Tg) mice. Moreover, reduction of atherosclerosis was significant in EC-miR-483 Tg mice administrated AAV8-PCSK9 and fed an atherogenic diet. In situ miR hybridization revealed an increased 8OH-G miR-483 level in the intima of human atherosclerotic arteries. Collectively, this study demonstrates that the redox burden incurred by cardiovascular risk factors is a culprit of the miR-483 to 8OH-G miR-483 transversion. Such epitranscriptomic modification of miR-483 causes endothelial dysfunction and increases atherosclerosis susceptibility via its targetomes shift.

molecular biology↗

bioRxiv2022

Mutant huntingtin exon-1 impaired GTPCH and DHFR expression in plants and mice

Pathophysiology associated with Huntingtons disease (HD) has been studied extensively in various cell and animal models since the 1993 discovery of the mutant huntingtin (mHtt) with abnormally expanded polyglutamine (polyQ) tracts as the causative factor. However, the sequence of early pathophysiological events leading to HD still remains elusive. To gain new insights into the polyQ-induced early pathogenic events, we expressed Htt exon1 (Httex1) with a normal (21), or an extended (42 or 63) number of polyQ in tobacco plants, which lack an Htt ortholog to avoid any associated effects from endogenous Htt. Here, we show that transgenic plants accumulated Httex1 proteins with corresponding polyQ tracts, and that mHttex1 induced protein aggregation and affected plant growth, especially root and root hair development, in a polyQ length-dependent manner. Quantitative proteomic analysis of young roots from severely affected Httex1Q63 and unaffected Httex1Q21 plants showed that the most impaired protein by polyQ63 is a GTP cyclohydrolase I (GTPCH) along with many its related one-carbon (C1) metabolic pathway enzymes. GTPCH is a key enzyme involved in folate biosynthesis in plants and tetrahydrobiopterin (BH4) biosynthesis in mammals. Validating studies in 4-week-old R6/2 HD mice expressing a mHttex1 showed reduced levels of GTPCH and dihydrofolate reductase (DHFR, a key folate utilization/alternate BH4 biosynthesis enzyme), and impaired C1 and BH4 metabolisms. Our findings from mHttex1 plants and mice reveal impaired expressions of GTPCH and DHFR and contribute to a better understanding of mHtt-altered C1 metabolism and C1 interconnected BH4 metabolism leading to the pathogenesis of HD.

molecular biology↗

bioRxiv2022

A landscape of differentiated biological processes involved in the initiation of sex differentiation in zebrafish

Zebrafish (Danio rerio) has been used as a promising animal model to study gonadal development and gametogenesis. Although previous studies have identified critical molecules participating in zebrafish gonad differentiation, a landscape view of the biological processes involved in this process is still lacking. Here we isolated intact zebrafish differentiating gonads, at 25 days post-fertilization (dpf) and 30 dpf, and conducted RNA-seq analysis between the juvenile gonads that tended to develop into ovaries or testes. Our study demonstrates that the juvenile ovary and testis at 25 dpf and 30 dpf are different at the level of biological process. During ovary differentiation, the biological processes related to metabolic activities in production of energy and maternal substances, RNA degradation, and DNA repair were enriched. During testis differentiation, the biological processes related to cell proliferation, differentiation, and morphogenesis were enriched, with a total of 15 signaling pathways. Notably, we reveal that the immune-related processes are extensively involved in the regulation of testis development. Overall, this study provides a landscape of differentiated biological processes and novel insights into the initiation of sex differentiation in zebrafish.

developmental biology↗

bioRxiv2022

Deep Brain Magnetothermal Silencing of Dopaminergic Neurons via Endogenous TREK1 Channels Abolishes Place Preference in Mice

Remote neuromodulation techniques have revolutionized our understanding of brain circuits and their role in behavior. The reversible silencing of specific neuronal populations has emerged as a powerful tool to investigate the necessity and sufficiency of these populations in behavioral responses. Here, we apply magnetothermal silencing using endogenous TREK-1 channels to selectively suppress dopaminergic reward in the ventral tegmental area (VTA) to prove that activation of this specific reward circuit is required for place preference in mice. Magnetothermal silencing entails the application of alternating magnetic fields that penetrate tissue, leading to the heating of superparamagnetic nanoparticles at the target cells, without causing any attenuation or adverse effects. The resultant slight, rapid, and reversible elevation in temperature effectively suppresses neuronal firing, without necessitating genetic modification of the neurons. We demonstrate that two-pore potassium channels, TREK-1, are responsible for this thermal neuronal silencing. Using fiber-based optogenetics we measured both the heating and neuronal silencing in the VTA brain of the animals. We show that in a place preference assay, magnetothermal neuronal silencing of the dopaminergic neurons in the VTA is sufficient to abolish the place preference. Notably, TREK1 knock-out mice exhibit immunity to magnetothermal silencing, behaving as if the magnetic field was not applied. These results underscore the critical role of dopaminergic neuronal activity in the VTA for the establishment of place preference and highlight the dependency on functional TREK1 channels in this magnetothermal silencing approach. HighlightsO_LITREK1 is a highly efficient, thermally activated neuronal silencer C_LIO_LIFirst magnetothermal neuronal silencing in behaving mice C_LIO_LIFiber photometry quantification of local heating and silencing of target neurons in the ventral tegmental area C_LIO_LIMagnetothermal suppression of dopaminergic reward response in the ventral tegmental area is sufficient to abolish place preference C_LI

neuroscience↗

bioRxiv2022

Direct and indirect neurogenesis generate a mosaic of distinct glutamatergic projection neuron types and cortical subnetworks

Variations in size and complexity of the cerebral cortex result from differences in neuron number and composition, which are rooted in evolutionary changes in direct and indirect neurogenesis (dNG and iNG) mediated by radial glial progenitors and intermediate progenitors, respectively. How dNG and iNG differentially contribute to cortical neuronal number, diversity, and connectivity are unknown. Establishing a genetic fate-mapping method to differentially visualize dNG and iNG in mice, we found that while both dNG and iNG contribute to all cortical structures, iNG contributes the largest relative proportions to the hippocampus and neocortex compared to insular and piriform cortex, claustrum, and the pallial amygdala. Within the neocortex, whereas dNG generates all major glutamatergic projection neuron (PN) classes, iNG differentially amplifies and diversifies PNs within each class; the two neurogenic pathways generate distinct PN types and assemble fine mosaics of lineage-based cortical subnetworks. Our results establish a ground-level lineage framework for understanding cortical development and evolution by linking foundational progenitor types and neurogenic pathways to PN types. Highlights- A genetic strategy for differential visualization of direct and indirect neurogenesis in the same animal. - dNG and iNG differentially contribute to piriform cortex, basolateral amygdala, hippocampus, and neocortex - Whereas dNG generates all major PN classes, iNG differentially amplifies and diversifies PNs within each class - dNG and iNG construct distinct cortical projection subnetworks.

neuroscience↗