Co-Expression Analysis of microRNAs and Proteins in Brain of Alzheimer’s Disease Patients

Alzheimer’s disease (AD) is the most common form of dementia globally; however, the aetiology of AD remains elusive hindering the development of effective therapeutics. MicroRNAs (miRNAs) are regulators of gene expression and have been of growing interest in recent studies in many pathologies including AD not only for their use as biomarkers but also for their implications in the therapeutic field. In this study, miRNA and protein profiles were obtained from brain tissues of different stage (Braak III-IV and Braak V-VI) of AD patients and compared to matched controls. The aim of the study was to identify in the late stage of AD, the key dysregulated pathways that may contribute to pathogenesis and then to evaluate whether any of these pathways could be detected in the early phase of AD, opening new opportunity for early treatment that could stop or delay the pathology. Six common pathways were found regulated by miRNAs and proteins in the late stage of AD, with one of them (Rap1 signalling) activated since the early phase. MiRNAs and proteins were also compared to explore an inverse trend of expression which could lead to the identification of new therapeutic targets. These results suggest that specific miRNA changes could represent molecular fingerprint of neurodegenerative processes and potential therapeutic targets for early intervention.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment

Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

Neuron-like function of the nephron central command

SUMMARYInteroceptive neurons that sense and regulate our internal milieu have been identified in several organs except in the kidney cortex despite its major importance in maintaining body homeostasis. Here we report that the chief kidney cell type of the macula densa (MD) forms coordinated neural networks in each nephron that resemble peripheral ganglia. A combined in vivo single-cell 4D physiology (sc4DP) and scRNA sequencing approach identified the MD mechanisms of neuronal differentiation, heterogeneity (pacemaker MD cells), sensing of the local and systemic environment via multi-organ crosstalk, and regulation of organ functions by acting as the nephron central command. Consistent with their neuron-like nature, MD cells express the molecular fingerprint of neurodegeneration. Here we put forth the single-cell MD model and concept of local neural networks that control organ and body functions via interoception in normal physiological state and use an integrated mechanism of neurodegeneration in disease.

Single cell RNA sequencing of the Strongylocentrotus purpuratus larva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome

Identifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.

Machine-learning Interpretation of the Correlation between Infrared Emission Features of Interstellar Polycyclic Aromatic Hydrocarbons

Supervised machine-learning models are trained with various molecular descriptors to predict infrared (IR) emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random forest algorithm can be utilized to explore the physical correlation between emission features. Astronomical correlations between IR bands are analyzed as examples of demonstration by finding the common molecular fragments responsible for different bands, which improves the current understanding of the long-observed correlations. We propose a way to quantify the band correlation by measuring the similarity of the feature importance arrays of different bands, by which a correlation map is obtained for emissions in the out-of-plane bending region. Moreover, a comparison between the predictions using different combinations of descriptors underscores the strong prediction power of the extended-connectivity molecular fingerprint, and shows that the combinations of multiple descriptors of other types in general lead to improved predictivity.

Involvement of MAP3K7 in FMD2 and CSCF, delineation of genotype/phenotype correlations.

Mitogen-Activated Protein 3 Kinase 7 (MAP3K7, MIM 602614) encodes the ubiquitously expressed transforming growth factor β (TGF-β)–activated kinase 1 (TAK1), which plays a crucial role in many cellular processes. Variants in the MAP3K7 gene have been linked to 2 distinct disorders: frontometaphyseal dysplasia type 2 (FMD2, MIM #617137) and cardiospondylocarpofacial syndrome (CSCF, MIM #157800). The fact that different variants can induce 2 distinct phenotypes suggests a phenotype/genotype correlation, but no side-by-side comparison has been done thus far to confirm this. Here we significantly expand the cohort and the description of clinical phenotypes for individuals with CSCF and FMD2 who carry variants in MAP3K7. We show that in contrast to FMD2-causing variants, CSCF-causing variants in MAP3K7 have a loss-of-function effect. Additionally, patients with pathogenic variants in MAP3K7 are at risk for cardiac disease, have symptoms associated with connective tissue disease and we show overlap in clinical phenotypes of CSCF with Noonan syndrome. Together, we provide evidence for a molecular fingerprint of FMD2- versus CSCF-causing MAP3K7 variants and conclude that variants in MAP3K7 should be considered in the differential diagnosis of patients with syndromic congenital cardiac defects and/or cardiomyopathy, syndromic connective tissue disorders and in the differential diagnosis of Noonan syndrome.

A graph neural network approach to molecule carcinogenicity prediction

Molecular carcinogenicity is a preventable cause of cancer, however, most experimental testing of molecular compounds is an expensive and time consuming process, making high throughput experimental approaches infeasible. In recent years, there has been substantial progress in machine learning techniques for molecular property prediction. In this work, we propose a model for carcinogenicity prediction, CONCERTO, which uses a graph transformer in conjunction with a molecular fingerprint representation, trained on multi-round mutagenicity and carcinogenicity objectives. To train and validate CONCERTO, we augment the training dataset with more informative labels and utilize a larger external validation dataset. Extensive experiments demonstrate that our model yields results superior to alternate approaches for molecular carcinogenicity prediction.