An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation.

Single-cell RNA sequencing (scRNA-seq) detects whole transcriptome signals for large amounts of individual cells and is powerful for determining cell-to-cell differences and investigating the functional characteristics of various cell types. scRNA-seq datasets are usually sparse and highly noisy. Many steps in the scRNA-seq analysis workflow, including reasonable gene selection, cell clustering and annotation, as well as discovering the underlying biological mechanisms from such datasets, are difficult. In this study, we proposed an scRNA-seq analysis method based on the latent Dirichlet allocation (LDA) model. The LDA model estimates a series of latent variables, i.e. putative functions (PFs), from the input raw cell-gene data. Thus, we incorporated the 'cell-function-gene' three-layer framework into scRNA-seq analysis, as this framework is capable of discovering latent and complex gene expression patterns via a built-in model approach and obtaining biologically meaningful results through a data-driven functional interpretation process. We compared our method with four classic methods on seven benchmark scRNA-seq datasets. The LDA-based method performed best in the cell clustering test in terms of both accuracy and purity. By analysing three complex public datasets, we demonstrated that our method could distinguish cell types with multiple levels of functional specialization, and precisely reconstruct cell development trajectories. Moreover, the LDA-based method accurately identified the representative PFs and the representative genes for the cell types/cell stages, enabling data-driven cell cluster annotation and functional interpretation. According to the literature, most of the previously reported marker/functionally relevant genes were recognized.

Impact of mutations in SARS-COV-2 spike on viral infectivity and antigenicity.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to alter the viral infectivity and antigenicity. Part of mutations in SARS-CoV-2 spike protein has conferred virus the ability to spread more quickly and escape from the immune response caused by the monoclonal neutralizing antibody or vaccination. Herein, we summarize the spatiotemporal distribution of mutations in spike protein, and present recent efforts and progress in investigating the impacts of those mutations on viral infectivity and antigenicity. As mutations continue to emerge in SARS-CoV-2, we strive to provide systematic evaluation of mutations in spike protein, which is vitally important for the subsequent improvement of vaccine and therapeutic neutralizing antibody strategies.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development.

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.

The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Metabonomics and Transcriptomics Analyses Reveal the Development Process of the Auditory System in the Embryonic Development Period of the Small Yellow Croaker under Background Noise.

Underwater noise pollution has become a potential threat to aquatic animals in the natural environment. The main causes of such pollution are frequent human activities creating underwater environmental noise, including commercial shipping, offshore energy platforms, scientific exploration activities, etc. However, in aquaculture environments, underwater noise pollution has also become an unavoidable problem due to background noise created by aquaculture equipment. Some research has shown that certain fish show adaptability to noise over a period of time. This could be due to fish's special auditory organ, i.e., their "inner ear"; meanwhile, otoliths and sensory hair cells are the important components of the inner ear and are also essential for the function of the auditory system. Recently, research in respect of underwater noise pollution has mainly focused on adult fish, and there is a lack of the research on the effects of underwater noise pollution on the development process of the auditory system in the embryonic development period. Thus, in this study, we collected embryo-larval samples of the small yellow croaker (Larimichthys polyactis) in four important stages of otic vesicle development through artificial breeding. Then, we used metabonomics and transcriptomics analyses to reveal the development process of the auditory system in the embryonic development period under background noise (indoor and underwater environment sound). Finally, we identified 4026 differentially expressed genes (DEGs) and 672 differential metabolites (DMs), including 37 DEGs associated with the auditory system, and many differences mainly existed in the neurula stage (20 h of post-fertilization/20 HPF). We also inferred the regulatory mode and process of some important DEGs (Dnmt1, CPS1, and endothelin-1) in the early development of the auditory system. In conclusion, we suggest that the auditory system development of L. polyactis begins at least in the neurula stage or earlier; the other three stages (tail bud stage, caudal fin fold stage, and heart pulsation stage, 28-35 HPF) mark the rapid development period. We speculate that the effect of underwater noise pollution on the embryo-larval stage probably begins even earlier.

Regional transcriptional vulnerability to basal forebrain functional dysconnectivity in mild cognitive impairment patients.

Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.

Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.

Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.

Natural epidithiodiketopiperazine alkaloids as potential anticancer agents: Recent mechanisms of action, structural modification, and synthetic strategies.

Cancer has become a grave health crisis that threatens the lives of millions of people worldwide. Because of the drawbacks of the available anticancer drugs, the development of novel and efficient anticancer agents should be encouraged. Epidithiodiketopiperazine (ETP) alkaloids with a 2,5-diketopiperazine (DKP) ring equipped with transannular disulfide or polysulfide bridges or S-methyl moieties constitute a special subclass of fungal natural products. Owing to their privileged sulfur units and intriguing architectural structures, ETP alkaloids exhibit excellent anticancer activities by regulating multiple cancer proteins/signaling pathways, including HIF-1, NF-κB, NOTCH, Wnt, and PI3K/AKT/mTOR, or by inducing cell-cycle arrest, apoptosis, and autophagy. Furthermore, a series of ETP alkaloid derivatives obtained via structural modification showed more potent anticancer activity than natural ETP alkaloids. To solve supply difficulties from natural resources, the total synthetic routes for several ETP alkaloids have been designed. In this review, we summarized several ETP alkaloids with anticancer properties with particular emphasis on their underlying mechanisms of action, structural modifications, and synthetic strategies, which will offer guidance to design and innovate potential anticancer drugs.

Low blood carotenoid status in dementia and mild cognitive impairment: A systematic review and meta-analysis.

BACKGROUND: Given their potent antioxidation properties, carotenoids play a role in delaying and preventing dementia and mild cognitive impairment (MCI). However, observational studies have found inconsistent results regarding the associations between blood carotenoid levels and the risk of dementia and MCI. We conducted this systematic review and meta-analysis to investigate the relationship between blood carotenoid levels and the risk of dementia and MCI. METHODS: A systematic search was performed in the Web of Science, PubMed, Embase, and Cochrane Library electronic databases to retrieve relevant English articles published from their inception until February 23, 2023. Study quality was assessed by the Newcastle-Ottawa scale. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were pooled using random-effect meta-analyses. Ultimately, 23 studies (n = 6610) involving 1422 patients with dementia, 435 patients with MCI, and 4753 controls were included. RESULTS: Our meta-analysis showed that patients with dementia had lower blood lycopene (SMD: -0.521; 95%CI: -0.741, -0.301), α-carotene (SMD: -0.489; 95%CI: -0.697, -0.281), ß-carotene (SMD: -0.476; 95%CI: -0.784, -0.168), lutein (SMD: -0.516; 95%CI: -0.753, -0.279), zeaxanthin (SMD: -0.571; 95%CI: -0.910, -0.232) and ß-cryptoxanthin (SMD: -0.617; 95%CI: -0.953, -0.281) than the controls. Our results indicated that blood carotenoid levels were significantly lower in patients with dementia than in controls, despite high heterogeneity across the studies. Owing to insufficient data, we did not observe a similar and stable relationship between blood carotenoid levels and MCI. CONCLUSIONS: Our meta-analysis indicated that lower blood carotenoid levels may be a risk factor for dementia and MCI.

Research progress of plant-derived natural alkaloids in central nervous system diseases.

Central nervous system (CNS) disease is one of the most important causes of human death. Because of their complex pathogenesis, more and more attention has been paid to them. At present, drug treatment of the CNS is the main means; however, most drugs only relieve symptoms, and some have certain toxicity and side effects. Natural compounds derived from plants can provide safer and more effective alternatives. Alkaloids are common nitrogenous basic organic compounds found in nature, which exist widely in many kinds of plants and have unique application value in modern medicine. For example, Galantamine and Huperzine A from medicinal plants are widely used drugs on the market to treat Alzheimer's disease. Therefore, the main purpose of this review is to provide the available information on natural alkaloids with the activity of treating central nervous system diseases in order to explore the trends and perspectives for the further study of central nervous system drugs. In this paper, 120 alkaloids with the potential effect of treating central nervous system diseases are summarized from the aspects of sources, structure types, mechanism of action and structure-activity relationship.

Prevalence and risk factors of self-reported financial toxicity in cancer survivors: A systematic review and meta-analyses.

BACKGROUND: Similar to the side effects of cancer treatment, financial toxicity (FT) can affect the quality of life of patients, which has attracted increasing attention in the field of oncology. Despite the fact that the estimated prevalence and risk factors of FT are widely reported, these results have not been synthesized. OBJECTIVES: This review is aimed to systematically assess the prevalence and risk factors of self-reported FT. DESIGN: Systematic review and meta-analyses. DATA SOURCES: A computer search of English literature was conducted using databases of PubMed, EMBASE, Web of Science, PsycINFO, and CINAHL, and reference lists of the qualified articles were also included between January 2010 and September 2021. Observational studies that reported the prevalence or risk factors of FT using subjective measures were included. METHODS: The systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The risk of bias was assessed by the NIH observational cohort and cross-sectional study quality assessment tool. The data were extracted by two reviewers and listed in a descriptive table for meta-analyses. RESULTS: In the 22 studies available for meta-analyses of pooled prevalence of FT, the result was estimated to be 45% (95% CI: 38% to 53%, I2 = 97.3%, P < 0.001) based on a random-effects model. The pooled analysis identified 9 potential risk factors of FT (7 in ß and 8 in OR): low income (OR = 2.48, 95% CI: 1.72 to 3.24, I2 = 3.1%, P < 0.001), greater annual OOP (ß = -4.26, 95% CI: -6.95 to -1.57, I2 = 0%, P = 0.002), younger age (OR = 2.05, 95% CI: 1.56 to 2.54, I2 = 0%, P < 0.001), no private insurance (OR = 1.69, 95% CI: 1.02 to 2.37, I2 = 0%, P < 0.001), unmarried (OR = 1.10, 95% CI: 0.95 to 1.25, I2 = 53,3%, P < 0.001), nonwhite (OR = 1.59, 95% CI: 1.33 to 1.85, I2 = 0%, P < 0.001), advanced cancer (ß = -4.74, 95% CI: -6.90 to -2.57, I2 = 0%, P < 0.001), unemployed (ß = -2.90, 95% CI: -5.71 to -0.63, I2 = 75,7%, P < 0.001), more recent diagnosis (OR = 1.31, 95% CI: 1.04 to 1.57, I2 = 0%, P < 0.001). CONCLUSION: This systematic review reported a pooled prevalence of self-reported FT of 45%. Low income, greater annual OOP (Out of pocket), younger age, unmarried, unemployed, nonwhite, no private insurance, advanced cancer, and more recent diagnosis constituted risk factors for self-reported FT. The research on risk factors for FT can provide a theoretical basis for medical staff to evaluate and intervene in the FT among cancer survivors.

Perceptions of Oncology Nurses Regarding Fertility Preservation and Providing Oncofertility Services for Men of Childbearing Age with Cancer.

This study investigated the knowledge, thoughts, and attitudes of oncology nurses in China regarding fertility preservation for male cancer patients of childbearing age, and for offering counseling or oncofertility services for the men in their care. Data was collected from 18 oncology nurses in Southwest China through voluntary self-report and in-depth interviews. The qualitative interview data were analyzed using a descriptive phenomenology method based on the lived experience of the nurses. The interviewees commonly reported 6 main concerns regarding fertility preservation (FP): their insufficient knowledge and inadequate nursing education; the importance of offering such services to cancer patients; legal vulnerability if FP information is withheld from patients; the role of the nurse in counseling; and barriers to discussing FP in practice. Nurses had a positive attitude toward FP, but most had no practical role in routinely informing male patients of their options, and the nurses believed that discussion of FP was outside their scope of practice. This study offers insight into the perceptions of oncology nurses in a developing country regarding the provision of FP services for adult male cancer patients. These results lead us to recommend that local fertility nurses should be given new training regarding FP. Furthermore, nurse-led clinics are desirable. Future research should focus on the effectiveness of nurse participation in FP counseling and referral, and how to improve the professional confidence of oncology nurses for addressing FP issues.

Identification of sub-Golgi protein localization by use of deep representation learning features.

MOTIVATION: The Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology. RESULTS: we developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction. AVAILABILITYAND IMPLEMENTATION: A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing.

T-cell receptor (TCR) is crucial in T cell-mediated virus clearance. To date, TCR bias has been observed in various diseases. However, studies on the TCR repertoire of COVID-19 patients are lacking. Here, we used single-cell V(D)J sequencing to conduct comparative analyses of TCR repertoire between 12 COVID-19 patients and 6 healthy controls, as well as other virus-infected samples. We observed distinct T cell clonal expansion in COVID-19. Further analysis of VJ gene combination revealed 6 VJ pairs significantly increased, while 139 pairs significantly decreased in COVID-19 patients. When considering the VJ combination of α and ß chains at the same time, the combination with the highest frequency on COVID-19 was TRAV12-2-J27-TRBV7-9-J2-3. Besides, preferential usage of V and J gene segments was also observed in samples infected by different viruses. Our study provides novel insights on TCR in COVID-19, which contribute to our understanding of the immune response induced by SARS-CoV-2.

Krüppel-like Transcription Factor 7 Is a Causal Gene in Autism Development.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disease. To date, more than 1000 genes have been shown to be associated with ASD, and only a few of these genes account for more than 1% of autism cases. Klf7 is an important transcription factor of cell proliferation and differentiation in the nervous system, but whether klf7 is involved in autism is unclear. METHODS: We first performed ChIP-seq analysis of klf7 in N2A cells, then performed behavioral tests and RNA-seq in klf7+/- mice, and finally restored mice with adeno-associated virus (AAV)-mediated overexpression of klf7 in klf7+/- mice. RESULTS: Klf7 targeted genes are enriched with ASD genes, and 631 ASD risk genes are also differentially expressed in klf7+/- mice which exhibited the core symptoms of ASD. When klf7 levels were increased in the central nervous system (CNS) in klf7+/- adult mice, deficits in social interaction, repetitive behavior and majority of dysregulated ASD genes were rescued in the adults, suggesting transcriptional regulation. Moreover, knockdown of klf7 in human brain organoids caused dysregulation of 517 ASD risk genes, 344 of which were shared with klf7+/- mice, including some high-confidence ASD genes. CONCLUSIONS: Our findings highlight a klf7 regulation of ASD genes and provide new insights into the pathogenesis of ASD and promising targets for further research on mechanisms and treatments.

Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment.

MOTIVATION: The multimodal data fusion analysis becomes another important field for brain disease detection and increasing researches concentrate on using neural network algorithms to solve a range of problems. However, most current neural network optimizing strategies focus on internal nodes or hidden layer numbers, while ignoring the advantages of external optimization. Additionally, in the multimodal data fusion analysis of brain science, the problems of small sample size and high-dimensional data are often encountered due to the difficulty of data collection and the specialization of brain science data, which may result in the lower generalization performance of neural network. RESULTS: We propose a genetically evolved random neural network cluster (GERNNC) model. Specifically, the fusion characteristics are first constructed to be taken as the input and the best type of neural network is selected as the base classifier to form the initial random neural network cluster. Second, the cluster is adaptively genetically evolved. Based on the GERNNC model, we further construct a multi-tasking framework for the classification of patients with brain disease and the extraction of significant characteristics. In a study of genetic data and functional magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative, the framework exhibits great classification performance and strong morbigenous factor detection ability. This work demonstrates that how to effectively detect pathogenic components of the brain disease on the high-dimensional medical data and small samples. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://github.com/lizi1234560/GERNNC.git.