Multi-omics delineate growth factor network underlying exercise effects in an Alzheimer's mouse model.

Physical exercise represents a primary defense against age-related cognitive decline and neurodegenerative disorders like Alzheimer's disease (AD). To impartially investigate the underlying mechanisms, we conducted single-nucleus transcriptomic and chromatin accessibility analyses (snRNA-seq and ATAC-seq) on the hippocampus of mice carrying AD-linked NL-G-F mutations in the amyloid precursor protein gene (APPNL-G-F) following prolonged voluntary wheel-running exercise. Our study reveals that exercise mitigates amyloid-induced changes in both transcriptomic expression and chromatin accessibility through cell type-specific transcriptional regulatory networks. These networks converge on the activation of growth factor signaling pathways, particularly the epidermal growth factor receptor (EGFR) and insulin signaling, correlating with an increased proportion of immature dentate granule cells and oligodendrocytes. Notably, the beneficial effects of exercise on neurocognitive functions can be blocked by pharmacological inhibition of EGFR and the downstream phosphoinositide 3-kinases (PI3K). Furthermore, exercise leads to elevated levels of heparin-binding EGF (HB-EGF) in the blood, and intranasal administration of HB-EGF enhances memory function in sedentary APPNL-G-F mice. These findings offer a panoramic delineation of cell type-specific hippocampal transcriptional networks activated by exercise and suggest EGF-related growth factor signaling as a druggable contributor to exercise-induced memory enhancement, thereby suggesting therapeutic avenues for combatting AD-related cognitive decline.

The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing.

RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation. The approach is designed specifically to suit clinical implementation, emphasizing simplicity, cost effectiveness, turnaround time, and reproducibility. For clinical validation, we generated induced neurons (iNeurons) from 71 individuals with primary neurological phenotypes recruited to the Undiagnosed Diseases Network. The overall diagnostic yield was 25.4%. Over a quarter of the diagnostic findings benefited from transdifferentiation and could not be achieved by fibroblast RNA-seq alone. This iNeuron transcriptomic approach can be effectively integrated into diagnostic whole-transcriptome evaluation of individuals with genetic disorders.

PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.

Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3' UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.

Quantification and visualization of cis-regulatory dynamics in single-cell multi-omics data with TREASMO.

Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.

MATR3 pathogenic variants differentially impair its cryptic splicing repression function.

Matrin-3 (MATR3) is an RNA-binding protein implicated in neurodegenerative and neurodevelopmental diseases. However, little is known regarding the role of MATR3 in cryptic splicing within the context of functional genes and how disease-associated variants impact this function. We show that loss of MATR3 leads to cryptic exon inclusion in many transcripts. We reveal that ALS-linked S85C pathogenic variant reduces MATR3 solubility but does not impair RNA binding. In parallel, we report a novel neurodevelopmental disease-associated M548T variant, located in the RRM2 domain, which reduces protein solubility and impairs RNA binding and cryptic splicing repression functions of MATR3. Altogether, our research identifies cryptic events within functional genes and demonstrates how disease-associated variants impact MATR3 cryptic splicing repression function.

CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model.

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12 000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpson's paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra.

MOTIVATION: Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. RESULTS: As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. AVAILABILITY AND IMPLEMENTATION: The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

A novel pathogenic mutation of MeCP2 impairs chromatin association independent of protein levels.

Loss-of-function mutations in MECP2 cause Rett syndrome (RTT), a severe neurological disorder that mainly affects girls. Mutations in MECP2 do occur in males occasionally and typically cause severe encephalopathy and premature lethality. Recently, we identified a missense mutation (c.353G>A, p.Gly118Glu [G118E]), which has never been seen before in MECP2, in a young boy who suffered from progressive motor dysfunction and developmental delay. To determine whether this variant caused the clinical symptoms and study its functional consequences, we established two disease models, including human neurons from patient-derived iPSCs and a knock-in mouse line. G118E mutation partially reduces MeCP2 abundance and its DNA binding, and G118E mice manifest RTT-like symptoms seen in the patient, affirming the pathogenicity of this mutation. Using live-cell and single-molecule imaging, we found that G118E mutation alters MeCP2's chromatin interaction properties in live neurons independently of its effect on protein levels. Here we report the generation and characterization of RTT models of a male hypomorphic variant and reveal new insight into the mechanism by which this pathological mutation affects MeCP2's chromatin dynamics. Our ability to quantify protein dynamics in disease models lays the foundation for harnessing high-resolution single-molecule imaging as the next frontier for developing innovative therapies for RTT and other diseases.

A cross-species proteomic map reveals neoteny of human synapse development.

The molecular mechanisms and evolutionary changes accompanying synapse development are still poorly understood1,2. Here we generate a cross-species proteomic map of synapse development in the human, macaque and mouse neocortex. By tracking the changes of more than 1,000 postsynaptic density (PSD) proteins from midgestation to young adulthood, we find that PSD maturation in humans separates into three major phases that are dominated by distinct pathways. Cross-species comparisons reveal that human PSDs mature about two to three times slower than those of other species and contain higher levels of Rho guanine nucleotide exchange factors (RhoGEFs) in the perinatal period. Enhancement of RhoGEF signalling in human neurons delays morphological maturation of dendritic spines and functional maturation of synapses, potentially contributing to the neotenic traits of human brain development. In addition, PSD proteins can be divided into four modules that exert stage- and cell-type-specific functions, possibly explaining their differential associations with cognitive functions and diseases. Our proteomic map of synapse development provides a blueprint for studying the molecular basis and evolutionary changes of synapse maturation.

Literature-based predictions of Mendelian disease therapies.

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

Physiological Mechanism of Waterlogging Stress on Yield of Waxy Maize at the Jointing Stage.

In the main agricultural area for waxy maize production in China, waterlogging occurs frequently during the waxy maize jointing stage, and this causes significant yield reduction. It is very important to understand the physiological mechanism of waterlogging stress in waxy maize during the jointing stage to develop strategies against waterlogging stress. Therefore, this study set waterlogging treatments in the field for 0, 2, 4, 6, 8, and 10 days during the waxy maize jointing stage, and were labelled CK, WS2, WS4, WS6, WS8 and WS10, respectively. By analyzing the effect of waterlogging on the source, sink, and transport of photoassimilates, the physiological mechanism of waterlogging stress in the jointing stage was clarified. The results show that PEPC and POD activities and Pro content decreased significantly under WS2 compared to CK. Except for these three indicators, the Pn, GS, leaf area, kernel number, yield, and puncture strength of stems were significantly decreased under the WS4. Under the WS6, the content of MDA began to increase significantly, while almost all other physiological indices decreased significantly. Moreover, the structure of stem epidermal cells and the vascular bundle were deformed after 6 days of waterlogging. Therefore, the threshold value of waterlogging stress occured at 4 to 6 days in the jointing stage of waxy maize. Moreover, waterlogging stress at the jointing stage mainly reduces the yield by reducing the number of kernels; specifically, the kernel number decreased by 6.7-15.5% in 4-10 days of waterlogging, resulting in a decrease of 9.9-20.2% in the final yield. Thus, we have shown that waterlogging stress at the jointing stage results in the decrease of potential waxy maize kernel numbers and yield when the synthesis of sources was limited and the transport of photoassimilates was restricted.

Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization.

SUMMARY: In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations. AVAILABILITY AND IMPLEMENTATION: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL.

Tau polarizes an aging transcriptional signature to excitatory neurons and glia.

Aging is a major risk factor for Alzheimer's disease (AD), and cell-type vulnerability underlies its characteristic clinical manifestations. We have performed longitudinal, single-cell RNA-sequencing in Drosophila with pan-neuronal expression of human tau, which forms AD neurofibrillary tangle pathology. Whereas tau- and aging-induced gene expression strongly overlap (93%), they differ in the affected cell types. In contrast to the broad impact of aging, tau-triggered changes are strongly polarized to excitatory neurons and glia. Further, tau can either activate or suppress innate immune gene expression signatures in a cell-type-specific manner. Integration of cellular abundance and gene expression pinpoints nuclear factor kappa B signaling in neurons as a marker for cellular vulnerability. We also highlight the conservation of cell-type-specific transcriptional patterns between Drosophila and human postmortem brain tissue. Overall, our results create a resource for dissection of dynamic, age-dependent gene expression changes at cellular resolution in a genetically tractable model of tauopathy.

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra.

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples.

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

A Comprehensive and Integrative Approach to MeCP2 Disease Transcriptomics.

Mutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2's molecular role. Individual transcriptomic studies yield inconsistent differentially expressed genes. To overcome these issues, we demonstrate a methodology to analyze all modern public data. We obtained relevant raw public transcriptomic data from GEO and ENA, then homogeneously processed it (QC, alignment to reference, differential expression analysis). We present a web portal to interactively access the mouse data, and we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found functionally distinct, consistently up- and downregulated subsets within these genes and some bias to their location. We present this common core of genes as well as focused cores for up, down, cell fraction models, and some tissues. We observed enrichment for this mouse core in other species MeCP2 models and observed overlap with ASD models. By integrating and examining transcriptomic data at scale, we have uncovered the true picture of this dysregulation. The vast scale of these data enables us to analyze signal-to-noise, evaluate a molecular signature in an unbiased manner, and demonstrate a framework for future disease focused informatics work.

Loss of the Maternal Effect Gene Nlrp2 Alters the Transcriptome of Ovulated Mouse Oocytes and Impacts Expression of Histone Demethylase KDM1B.

The subcortical maternal complex (SCMC) is a multiprotein complex in oocytes and preimplantation embryos that is encoded by maternal effect genes. The SCMC is essential for zygote-to-embryo transition, early embryogenesis, and critical zygotic cellular processes, including spindle positioning and symmetric division. Maternal deletion of Nlrp2, which encodes an SCMC protein, results in increased early embryonic loss and abnormal DNA methylation in embryos. We performed RNA sequencing on pools of meiosis II (MII) oocytes from wild-type and Nlrp2-null female mice that were isolated from cumulus-oocyte complexes (COCs) after ovarian stimulation. Using a mouse reference genome-based analysis, we found 231 differentially expressed genes (DEGs) in Nlrp2-null compared to WT oocytes (123 up- and 108 downregulated; adjusted p < 0.05). The upregulated genes include Kdm1b, a H3K4 histone demethylase required during oocyte development for the establishment of DNA methylation marks at CpG islands, including those at imprinted genes. The identified DEGs are enriched for processes involved in neurogenesis, gland morphogenesis, and protein metabolism and for post-translationally methylated proteins. When we compared our RNA sequencing data to an oocyte-specific reference transcriptome that contains many previously unannotated transcripts, we found 228 DEGs, including genes not identified with the first analysis. Interestingly, 68% and 56% of DEGs from the first and second analyses, respectively, overlap with oocyte-specific hyper- and hypomethylated domains. This study shows that there are substantial changes in the transcriptome of mouse MII oocytes from female mice with loss of function of Nlrp2, a maternal effect gene that encodes a member of the SCMC.

scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data.

Motivation: With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity. Results: Here, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurement L. The graph-based correlation method assigns each cell a local L index, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson's r in ruling out confounding regulatory relationships that are not of research interests. Availability: https://github.com/ChaozhongLiu/scGREAT.