Abstract

RNAs are the major regulators of gene expression, and their secondary structures play crucial roles at different levels. RiboSNitches are disease-associated SNPs that cause changes in the pre-mRNA secondary structural ensemble. Several riboSNitches have been detected in the 5′ and 3′ untranslated regions and lncRNA. Although cases of secondary structural elements playing a regulatory role in alternative splicing are known, regions specific to splicing events, such as splice junctions have not received much attention. We tested splice-site mutations for their efficiency in disrupting the secondary structure and hypothesized that these could play a crucial role in alternative splicing. Multiple riboSNitch prediction methods were applied to obtain overlapping results that are potentially more reliable. Putative riboSNitches were identified from aberrant 5′ and 3′ splice site mutations, cancer-causing somatic mutations, and genes that harbor the regulatory RNA secondary structural elements. Our workflow for predicting riboSNitches associated with alternative splicing is novel and paves the way for subsequent experimental validation.

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder predominantly found in children. The current behavior-based diagnosis of ASD is arduous and requires expertise. Therefore, it is appealing to develop an accurate computer-aided tool for diagnosing ASD. Although resting-state functional magnetic resonance imaging (rsfMRI) has proven to be successful in capturing the neural organization of the brain, automated detection of ASD using rsfMRI scans is a challenging task due to heterogeneity in the dataset and limited sample size. This paper proposes a Multilayer Perceptron (MLP) based classification model with auto encoder pretraining for classifying ASD from Typically Developing (TD) using rsfMRI scans obtained from the ABIDE-1 dataset. Our model achieves new state-of-the-art performance on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 74.82%. Further, we use the Integrated Gradients (IG) and DeepLIFT techniques to identify the correlations between brain regions that contribute most to the classification task. Our analysis identifies the following regions, Left Lingual Gyrus, Right Insula Lobe, Right Cuneus, Right Middle Frontal Gyrus, Left Superior Temporal Gyrus to be associated with ASD. Interestingly, these regions in the brain are primarily responsible for social cognition, language, attention, decision making and visual processing, which are known to be altered in ASD.

Abstract

During healthy ageing, the brain networks undergo various topological and functional alterations. Previous studies have shown that the dedifferentiation of the functional modules could be one of the hallmarks of large-scale brain networks and alterations through the lifespan. This modular organiza- tion and alterations may be critically linked to a variety of neurodegenerative disorders and cognitive deficits encountered during ageing. In spite of accumulating evidence based on tracking static functional connectivity (FC) and modularity in characterizing dedifferentiation associated with ageing, there is a gap in understanding the brain dynamics of modular segregation and integration through the lifespan. Using the Cam-CAN dataset (young: 18-44, mean 32 years, old: 65-88, mean 75 years), we characterize the modular reorganization using dynamic measures like flexibility, to find characteristic nodes that make up the stable core and flexible periphery in the young and old age groups. In this study, we hypothesize that the nodes that exhibit higher flexibility in the older age groups will be negatively correlated with modularity since these nodes ‘compensate’ for the functional integration while ensuring that the segregation is efficient. Our results demonstrate that the regions from the Default Mode network (DMN) show a negative correlation with modularity in the old age groups. Further, nodes from Limbic, SensoriMotor (SMN) and Salience networks show a positive correlation with modularity. These networks that are responsible for higher-order cognitive functions, e.g., decision making, attentional control, cognitive flexibility are found to make up a stable core as evidenced by their low flexibility scores. We also trained various classifiers using node flexibility scores as features for the binary (young vs old) classification task. Support Vector Machine (SVM) with Gaussian kernel trained on a reduced-dimensional feature set gave the best classification results. The features (nodes) that are found to be important for classification concur with those identified through the data-driven network measures based analysis. In summary, we anticipate that these findings can help identify the regions that are responsible for the reorganization

Abstract

The role of non-coding RNAs in neuropsychiatric disorders (NPDs) is an emerging field of study. The long non-coding RNAs (lncRNAs) are shown to sponge the microRNAs (miRNAs) from interacting with their target mRNAs. Investigating the sponge activity of lncRNAs in NPDs will provide further insights into biological mechanisms and help identify disease biomarkers. In this study, a large-scale inference of the lncRNA-related miRNA sponge network of pan-neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), was carried out using brain transcriptomic (RNA-Seq) data. The candidate miRNA sponge modules were identified based on the co-expression pattern of non-coding RNAs, sharing of miRNA binding sites, and sensitivity canonical correlation. miRNA sponge modules are associated with chemical synaptic transmission, nervous system development, metabolism, immune system response, ribosomes, and pathways in cancer. The identified modules showed similar and distinct gene expression patterns depending on the neuropsychiatric condition. The preservation of miRNA sponge modules was shown in the independent brain and blood-transcriptomic datasets of NPDs. We also identified miRNA sponging lncRNAs that may be potential diagnostic biomarkers for NPDs. Our study provides a comprehensive resource on miRNA sponging in NPDs.

Abstract

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and dierent neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also aected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject’s age and classified them into dierent age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r 2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how dierent functional regions of the brain are correlated. We also analyzed how functional regions contribute dierently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing

Abstract

Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and proliferation. The liver has a remarkable capacity to regenerate after injury or resection by cell growth and division. During regeneration, the liver needs to maintain the essential metabolic tasks and meet the metabolic requirements for hepatocyte growth and division. Understanding the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection is crucial. In this study, we analyzed high-resolution temporal RNA sequencing data of liver regeneration after two-thirds partial hepatectomy (PHx) using network inference and mathematical modeling approaches. The reconstruction of the dynamic regulatory network of liver regeneration reveals the trajectories of different metabolic pathways, protein processing in the endoplasmic reticulum (ER), ribosome biogenesis, RNA transport, spliceosome, immune response, and cell cycle. We further developed a mathematical model of the integrated circuit of liver regeneration that accountsfor underlying features of compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition during liver regeneration. We show that a mutually exclusive behavior emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level expressions observed in single-cell RNA sequencing data of liver regeneration.

Abstract

Application of deep learning techniques for de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in natural language processing, such as Transformers, for molecular design in general. Inspired by generative pre-training (GPT) models that have been shown to be successful in generating meaningful text, we train a transformer-decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, MolGPT, performs on par with other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique, and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to

Abstract

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner

Abstract

The advent of digital pathology presents opportunities for computer vision for fast, accurate, and objective solutions for histopathological images and aid in knowledge discovery. This work uses deep learning to predict genomic biomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status, and intrinsic subtypes, from breast cancer histopathology images. Furthermore, we attempt to understand the underlying morphology as to how these genomic biomarkers manifest in images. Since gene sequencing is expensive, not always available, or even feasible, predicting these biomarkers from images would help in diagnosis, prognosis, and effective treatment planning. We outperform the existing works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC scores across all tasks. We also gain insights that can serve as hypotheses for further experimentations, including the presence of lymphocytes and karyorrhexis. Moreover, our fully automated workflow can be extended to other tasks across other cancer subtypes.

Abstract

Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARSCoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genomescale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.