RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization.

Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to the subcellular localizations of mRNAs and lncRNAs. With the Tree SHAP algorithm, RNAlight extracts nucleotide features for cytoplasmic or nuclear localization of RNAs, indicating the sequence basis for distinct RNA subcellular localizations. By assembling k-mers to sequence features and subsequently mapping to known RBP-associated motifs, different types of sequence features and their associated RBPs were additionally uncovered for lncRNAs and mRNAs with distinct subcellular localizations. Finally, we extended RNAlight to precisely predict the subcellular localizations of other types of RNAs, including snRNAs, snoRNAs and different circular RNA transcripts, suggesting the generality of using RNAlight for RNA subcellular localization prediction.

Human-unique brain cell clusters are associated with learning disorders and human episodic memory activity.

The advanced evolution of the human cerebral cortex forms the basis for our high-level cognitive functions. Through a comparative analysis of single-nucleus transcriptome data from the human neocortex and that of chimpanzees, macaques, and marmosets, we discovered 20 subgroups of cell types unique to the human brain, which include 11 types of excitatory neurons. Many of these human-unique cell clusters exhibit significant overexpression of genes regulated by human-specific enhancers. Notably, these specific cell clusters also express genes associated with disease risk, particularly those related to brain dysfunctions like learning disorders. Furthermore, genes linked to cortical thickness and human episodic memory encoding activities show heightened expression within these cell subgroups. These findings underscore the critical role of human brain-unique cell clusters in the evolution of human brain functions.

[Active ingredients and in vitro anti-inflammatory effects of Polygoni Cuspidati Rhizoma et Radix decoction pieces by integrated processing and traditional processing: a comparative analysis].

The present study explored the differences in active ingredients and in vitro anti-inflammatory effects of the decoction pieces by integrated processing(IPDP) and traditional processing(TPDP) of Polygoni Cuspidati Rhizoma et Radix(PCRER).The content of polydatin, resveratrol, emodin-8-O-ß-D-glucoside, emodin, and physcion in IPDP and TPDP was determined by high-performance liquid chromatography(HPLC).The inflammation model was induced by lipopolysaccharide(LPS) in RAW264.7 cells.The mRNA levels of inflammatory cytokines tumor necrosis factor-α(TNF-α), interleukin-6(IL-6), and interleukin-1ß(IL-1ß) in 60% ethanol extracts of IPDP and TPDP of different concentrations(5 and 10 µg·mL~(-1)) were determined by PCR.The results showed that the content of polydatin and emodin-8-O-ß-D-glucoside in IPDP was significantly higher than that in TPDP, while the content of resveratrol, emodin, and physcion was higher in TPDP.The anti-inflammatory results showed that ethanol extracts of IPDP of different concentrations(5 and 10 µg·mL~(-1)) significantly inhibited the increase in the mRNA levels of IL-1ß and TNF-α induced by LPS, whereas TPDP only had a significant inhibitory effect on IL-1ß.This study preliminarily showed that the total content of five active ingredients in IPDP was higher than that in TPDP, and IPDP was superior to TPDP in anti-inflammatory activity in vitro, which provided an experimental basis for the production and application of IPDP.

MECP2 Duplication Causes Aberrant GABA Pathways, Circuits and Behaviors in Transgenic Monkeys: Neural Mappings to Patients with Autism.

MECP2 gain-of-function and loss-of-function in genetically engineered monkeys recapitulates typical phenotypes in patients with autism, yet where MECP2 mutation affects the monkey brain and whether/how it relates to autism pathology remain unknown. Here we report a combination of gene-circuit-behavior analyses including MECP2 coexpression network, locomotive and cognitive behaviors, and EEG and fMRI findings in 5 MECP2 overexpressed monkeys (Macaca fascicularis; 3 females) and 20 wild-type monkeys (Macaca fascicularis; 11 females). Whole-genome expression analysis revealed MECP2 coexpressed genes significantly enriched in GABA-related signaling pathways, whereby reduced ß-synchronization within fronto-parieto-occipital networks was associated with abnormal locomotive behaviors. Meanwhile, MECP2-induced hyperconnectivity in prefrontal and cingulate networks accounted for regressive deficits in reversal learning tasks. Furthermore, we stratified a cohort of 49 patients with autism and 72 healthy controls of 1112 subjects using functional connectivity patterns, and identified dysconnectivity profiles similar to those in monkeys. By establishing a circuit-based construct link between genetically defined models and stratified patients, these results pave new avenues to deconstruct clinical heterogeneity and advance accurate diagnosis in psychiatric disorders.SIGNIFICANCE STATEMENT Autism spectrum disorder (ASD) is a complex disorder with co-occurring symptoms caused by multiple genetic variations and brain circuit abnormalities. To dissect the gene-circuit-behavior causal chain underlying ASD, animal models are established by manipulating causative genes such as MECP2 However, it is unknown whether such models have captured any circuit-level pathology in ASD patients, as demonstrated by human brain imaging studies. Here, we use transgenic macaques to examine the causal effect of MECP2 overexpression on gene coexpression, brain circuits, and behaviors. For the first time, we demonstrate that the circuit abnormalities linked to MECP2 and autism-like traits in the monkeys can be mapped to a homogeneous ASD subgroup, thereby offering a new strategy to deconstruct clinical heterogeneity in ASD.

Structural analysis of fungal CENP-H/I/K homologs reveals a conserved assembly mechanism underlying proper chromosome alignment.

The kinetochore is a proteinaceous complex that is essential for proper chromosome segregation. As a core member of the inner kinetochore, defects of each subunit in the CENP-H/I/K complex cause dysfunction of kinetochore that leads to chromosome mis-segregation and cell death. However, how the CENP-H/I/K complex assembles and promotes kinetochore function are poorly understood. We here determined the crystal structures of CENP-I N-terminus alone from Chaetomium thermophilum and its complex with CENP-H/K from Thielavia terrestris, and verified the identified interactions. The structures and biochemical analyses show that CENP-H and CENP-K form a heterodimer through both N- and C-terminal interactions. CENP-I integrates into the CENP-H/K complex by binding to the C-terminus of CENP-H, leading to formation of the ternary complex in which CENP-H is sandwiched between CENP-K and CENP-I. Our sequence comparisons and mutational analyses showed that this architecture of the CENP-H/I/K complex is conserved in human. Mutating the binding interfaces of CENP-H for either CENP-K or CENP-I significantly reduced their localizations at centromeres and induced massive chromosome alignment defects during mitosis, suggesting that the identified interactions are critical for CENP-H/I/K complex assembly at the centromere and kinetochore function. Altogether, our findings unveil the evolutionarily conserved assembly mechanism of the CENP-H/I/K complex that is critical for proper chromosome alignment.

A single factor dominates the behavior of rhythmic genes in mouse organs.

BACKGROUND: Circadian rhythm, regulated by both internal and external environment of the body, is a multi-scale biological oscillator of great complexity. On the molecular level, thousands of genes exhibit rhythmic transcription, which is both organ- and species-specific, but it remains a mystery whether some common factors could potentially explain their rhythmicity in different organs. In this study we address this question by analyzing the transcriptome data in 12 mouse organs to determine such major impacting factors. RESULTS: We found a strong positive correlation between the transcriptional level and rhythmic amplitude of circadian rhythmic genes in mouse organs. Further, transcriptional level could explain over 70% of the variation in amplitude. In addition, the functionality and tissue specificity were not strong predictors of amplitude, and the expression level of rhythmic genes was linked to the energy consumption associated with transcription. CONCLUSION: Expression level is a single major factor impacts the behavior of rhythmic genes in mouse organs. This single determinant implicates the importance of rhythmic expression itself on the design of the transcriptional system. So, rhythmic regulation of highly expressed genes can effectively reduce the energetic cost of transcription, facilitating the long-term adaptive evolution of the entire genetic system.

Human Genomic Signatures of Brain Oscillations During Memory Encoding.

Memory encoding is an essential step for all learning. However, the genetic and molecular mechanisms underlying human memory encoding remain poorly understood, and how this molecular framework permits the emergence of specific patterns of brain oscillations observed during mnemonic processing is unknown. Here, we directly compare intracranial electroencephalography recordings from the neocortex in individuals performing an episodic memory task with human gene expression from the same areas. We identify genes correlated with oscillatory memory effects across 6 frequency bands. These genes are enriched for autism-related genes and have preferential expression in neurons, in particular genes encoding synaptic proteins and ion channels, supporting the idea that the genes regulating voltage gradients are involved in the modulation of oscillatory patterns during successful memory encoding across brain areas. Memory-related genes are distinct from those correlated with other forms of cognitive processing and resting state fMRI. These data are the first to identify correlations between gene expression and active human brain states as well as provide a molecular window into memory encoding oscillations in the human brain.

Dscam1 web server: online prediction of Dscam1 self- and hetero-affinity.

MOTIVATION: Formation of homodimers by identical Dscam1 protein isomers on cell surface is the key factor for the self-avoidance of growing neurites. Dscam1 immense diversity has a critical role in the formation of arthropod neuronal circuit, showing unique evolutionary properties when compared to other cell surface proteins. Experimental measures are available for 89 self-binding and 1722 hetero-binding protein samples, out of more than 19 thousands (self-binding) and 350 millions (hetero-binding) possible isomer combinations. RESULTS: We developed Dscam1 Web Server to quickly predict Dscam1 self- and hetero- binding affinity for batches of Dscam1 isomers. The server can help the study of Dscam1 affinity and help researchers navigate through the tens of millions of possible isomer combinations to isolate the strong-binding ones. AVAILABILITY AND IMPLEMENTATION: Dscam1 Web Server is freely available at: http://bioinformatics.tecnoparco.org/Dscam1-webserver . Web server code is available at https://gitlab.com/ne1s0n/Dscam1-binding . CONTACT: simone.marini@unipv.it or guangzhong.wang@picb.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improvement of Dscam homophilic binding affinity throughout Drosophila evolution.

BACKGROUND: Drosophila Dscam1 is a cell-surface protein that plays important roles in neural development and axon tiling of neurons. It is known that thousands of isoforms bind themselves through specific homophilic interactions, a process which provides the basis for cellular self-recognition. Detailed biochemical studies of specific isoforms strongly suggest that homophilic binding, i.e. the formation of homodimers by identical Dscam1 isomers, is of great importance for the self-avoidance of neurons. Due to experimental limitations, it is currently impossible to measure the homophilic binding affinities for all 19,000 potential isoforms. RESULTS: Here we reconstructed the DNA sequences of an ancestral Dscam form (which likely existed approximately 40 ~ 50 million years ago) using a comparative genomic approach. On the basis of this sequence, we established a working model to predict the self-binding affinities of all isoforms in both the current and the ancestral genome, using machine-learning methods. Detailed computational analysis was performed to compare the self-binding affinities of all isoforms present in these two genomes. Our results revealed that 1) isoforms containing newly derived variable domains exhibit higher self-binding affinities than those with conserved domains, and 2) current isoforms display higher self-binding affinities than their counterparts in the ancient genome. As thousands of Dscam isoforms are needed for the self-avoidance of the neuron, we propose that an increase in self-binding affinity provides the basis for the successful evolution of the arthropod brain. CONCLUSIONS: Our data presented here provide an excellent model for future experimental studies of the binding behavior of Dscam isoforms. The results of our analysis indicate that evolution favored the rise of novel variable domains thanks to their higher self-binding affinities, rather than selection merely on the basis of simple expansion of isoform diversity, as that this particular selection process would have established the powerful mechanisms required for neuronal self-avoidance. Thus, we reveal here a new molecular mechanism for the successful evolution of arthropod brains.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing.

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions.

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level.

Dominant constraints on the evolution of rhythmic gene expression.

Although the individual transcriptional regulators of the core circadian clock are distinct among different organisms, the autoregulatory feedback loops they form are conserved. This unified design principle explains how daily physiological activities oscillate across species. However, it is unknown whether analogous design principles govern the gene expression output of circadian clocks. In this study, we performed a comparative analysis of rhythmic gene expression in eight diverse species and identified four common distribution patterns of cycling gene expression across these species. We hypothesized that the maintenance of reduced energetic costs constrains the evolution of rhythmic gene expression. Our large-scale computational simulations support this hypothesis by showing that selection against high-energy expenditure completely regenerates all cycling gene patterns. Moreover, we find that the peaks of rhythmic expression have been subjected to this type of selective pressure. The results suggest that selective pressure from circadian regulation efficiently removes unnecessary gene products from the transcriptome, thereby significantly impacting its evolutionary path.

scBrainMap: a landscape for cell types and associated genetic markers in the brain.

The great variety of brain cell types is a fundamental element for neuronal circuits. One major goal of modern neuroscience is to decipher the various types of cellular composition and characterize their properties. Due to the high heterogeneity of neuronal cells, until recently, it was not possible to group brain cell types at high resolution. Thanks to the single-cell transcriptome technology, a dedicated database of brain cell types across species has been established. Here, we developed scBrainMap, a database for brain cell types and associated genetic markers for several species. The current scBrainMap database contains 4881 cell types with 26 044 genetic markers identified from 6 577 222 single cells, which link to 14 species, 124 brain regions and 20 different disease states. scBrainMap enables users to perform customized, cross-linked, biologically relevant queries for different cell types of interest. This quantitative information facilitates exploratory research on the role of cell types with regard to brain function in health and disease. Database URL https://scbrainmap.sysneuro.net/.

mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants.

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed Pathogenicity Prediction Tool for missense variants (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at http://www.mvppt.club/.

Transcriptomic profiling of nuclei from paraformaldehyde-fixed and formalin-fixed paraffin-embedded brain tissues.

BACKGROUND: Paraformaldehyde (PFA) fixation is necessary for histochemical staining, and formalin-fixed and paraffin-embedded (FFPE) tissue archives are the largest repository of clinically annotated specimens. Single-cell gene expression workflows have recently been developed for PFA-fixed and FFPE tissue specimens. However, for tissues where intact cells are hard to recover, including tissues containing highly interconnected neurons, single-nuclear transcriptomics is beneficial. Moreover, since RNA is very unstable, the effects of standard pathological practice on the transcriptome of samples obtained from such archived specimens like FFPE samples are largely anecdotal. RESULTS: We evaluated the effects of polyformaldehyde (PFA) fixation and paraffin-embedding on transcriptional profiles of the mouse hippocampus obtained by RNA sequencing (RNA-seq). The transcriptomic signatures of nuclei isolated from fresh PFA-fixed and fresh FFPE tissues were comparable to those of cryopreserved samples. However, more differentially expressed genes were obtained for brains after PFA fixation for more than 3 days than in fresh PFA-fixed samples, especially genes involved in spliceosome and synaptic-related pathways. Importantly, the real cell states were destroyed, with oligodendrocyte precursor cells depleted in the 1day fixed hippocampus. After fixation for 3 days, the proportions of neuronal cells and oligodendrocytes decreased and microglia increased; however, relative frequencies remained constant for longer fixation durations. The storage time of FFPE samples had a negligible effect on the cell composition. SIGNIFICANCE: This represents the first work to investigate the effects of fixation and storage time of brains on its nuclear transcriptome signatures in detail. The fixation time had more influences on the nuclear transcriptomic profiles than FFPE retention time, and the cliff-like effects appeared to occur over a fixed period of 1-3 days. These findings are expected to guide sample preparation for single-nucleus RNA-seq of FFPE samples, particularly in transcriptomic studies focused on brain diseases.

Cynomolgus-rhesus hybrid macaques serve as a platform for imprinting studies.

Genomic imprinting can lead to allele-specific expression (ASE), where one allele is preferentially expressed more than the other. Perturbations in genomic imprinting or ASE genes have been widely observed across various neurological disorders, notably autism spectrum disorder (ASD). In this study, we crossed rhesus cynomolgus monkeys to produce hybrid monkeys and established a framework to evaluate their allele-specific gene expression patterns using the parental genomes as a reference. Our proof-of-concept analysis of the hybrid monkeys identified 353 genes with allele-biased expression in the brain, enabling us to determine the chromosomal locations of ASE clusters. Importantly, we confirmed a significant enrichment of ASE genes associated with neuropsychiatric disorders, including ASD, highlighting the potential of hybrid monkey models in advancing our understanding of genomic imprinting.

Noncoding transcripts are linked to brain resting-state activity in non-human primates.

Brain-derived transcriptomes are known to correlate with resting-state brain activity in humans. Whether this association holds in nonhuman primates remains uncertain. Here, we search for such molecular correlates by integrating 757 transcriptomes derived from 100 macaque cortical regions with resting-state activity in separate conspecifics. We observe that 150 noncoding genes explain variations in resting-state activity at a comparable level with protein-coding genes. In-depth analysis of these noncoding genes reveals that they are connected to the function of nonneuronal cells such as oligodendrocytes. Co-expression network analysis finds that the modules of noncoding genes are linked to both autism and schizophrenia risk genes. Moreover, genes associated with resting-state noncoding genes are highly enriched in human resting-state functional genes and memory-effect genes, and their links with resting-state functional magnetic resonance imaging (fMRI) signals are altered in the brains of patients with autism. Our results highlight the potential for noncoding RNAs to explain resting-state activity in the nonhuman primate brain.

MiR-122-5p regulates the mevalonate pathway by targeting p53 in non-small cell lung cancer.

The 5-year survival rate of non-small cell lung cancer (NSCLC) patients is very low. MicroRNAs (miRNAs) are involved in the occurrence of NSCLC. miR-122-5p interacts with wild-type p53 (wtp53), and wtp53 affects tumor growth by inhibiting the mevalonate (MVA) pathway. Therefore, this study aimed to evaluate the role of these factors in NSCLC. The role of miR-122-5p and p53 was established in samples from NSCLC patients, and human NSCLC cells A549 using the miR-122-5p inhibitor, miR-122-5p mimic, and si-p53. Our results showed that inhibiting miR-122-5p expression led to the activation of p53. This inhibited the progression of the MVA pathway in the NSCLC cells A549, hindered cell proliferation and migration, and promoted apoptosis. miR-122-5p was negatively correlated with p53 expression in p53 wild-type NSCLC patients. The expression of key genes in the MVA pathway in tumors of p53 wild-type NSCLC patients was not always higher than the corresponding normal tissues. The malignancy of NSCLC was positively correlated with the high expression of the key genes in the MVA pathway. Therefore, miR-122-5p regulated NSCLC by targeting p53, providing potential molecular targets for developing targeted drugs.

Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys.

Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.

Gene networks under circadian control exhibit diurnal organization in primate organs.

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and their downstream targets across time. While interactions between these molecular clocks are necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8120) of baboon genes are oscillating body-wide. Additionally, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, a majority of SARS-CoV-2-related genes and modules are rhythmically expressed, which have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.