Mitochondria and G-quadruplex evolution: an intertwined relationship.

G-quadruplexes (G4s) are non-canonical structures formed in guanine (G)-rich sequences through stacked G tetrads by Hoogsteen hydrogen bonding. Several studies have demonstrated the existence of G4s in the genome of various organisms, including humans, and have proposed that G4s have a regulatory role in various cellular functions. However, little is known regarding the dissemination of G4s in mitochondria. In this review, we report the observation that the number of potential G4-forming sequences in the mitochondrial genome increases with the evolutionary complexity of different species, suggesting that G4s have a beneficial role in higher-order organisms. We also discuss the possible function of G4s in mitochondrial (mt)DNA and long noncoding (lnc)RNA and their role in various biological processes.

Artificial intelligence in microbial natural product drug discovery: current and emerging role.

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Discovery of 3-(benzofuran-2-ylmethyl)-1H-indole derivatives as potential autophagy inducers in cervical cancer cells.

In this manuscript we have documented the identification of a novel anticancer scaffold 3-(benzofuran-2-ylmethyl)-1H-indole. This scaffold has been designed by tweaking the known bisindolylmethane scaffold of natural products that display a wide range of biological activities. A series of 24 new conjugates have been synthesized and among them 5 derivatives exhibited IC50 values less than 40 µM against two cervical cancer cell lines SiHa and C33a. Further mechanistic studies of two compounds 3eb and 3ec revealed that the toxicity of these compounds was due to the effective induction of autophagy mediated cell death. The autophagy induction was confirmed by the progressive conversion of LC3I to LC3II and downregulation of p62 in cervical cancer cells.

RNA G-quadruplex in functional regulation of noncoding RNA: Challenges and emerging opportunities.

G-quadruplexes (G4s) are stable, noncanonical structures formed in guanine (G)-rich sequences of DNA/RNA. G4 structures are reported to play a regulatory role in various cellular processes and, recently, a considerable number of studies have attributed new biological functions to these structures, especially in RNA. Noncoding RNA (ncRNA), which does not translate into a functional protein, is widely expressed and has been shown to play a key role in shaping cellular activity. There has been growing evidence of G4 formation in several ncRNA classes, and it has been identified as a key part for diverse biological functions and physio-pathological contexts in neurodegenerative diseases and cancer. This review discusses RNA G4s (rG4s) in ncRNA, focusing on the molecular mechanism underlying its function. This review also aims to highlight potential and emerging opportunities to identify and target the rG4s in ncRNA to understand its function and, ultimately, treat many diseases.

Discoidin Domain Receptor 1 Inhibitors: Advances and Future Directions for Novel Therapeutics with Aid of DNA Encoded Library Screens and Artificial Intelligence.

Discoidin domain receptor (DDR) 1, a collagen binding receptor kinase, is an intensively researched therapeutic target for cancer, fibrosis and other diseases. The majority of early known DDR1 inhibitors targeted the ATP binding pocket of this enzyme that shares structural similarities with other kinase pockets across the biological system. This structural similarity of DDR1 kinase with other protein kinases often leads to "off target "toxicity issues. Understanding of uniqueness in DDR:ATP-phosphate-binding loop (P-loop), DNA encoded library screen, structure-guided optimization studies, and machine learning drug design platforms that come under the umbrella of artificial intelligence has led to the discovery of a new array of inhibitors that are highly selective for DDR1 over DDR2 and other similar kinases. Most of the drug discovery platforms concentrated on the ATP binding region of DDR1 kinase and never looked beyond this region for novel therapeutic options. Recent findings have disclosed the kinase-independent functions of DDR1 in immune exclusion, which resides in the extracellular collagen-binding domain, thus opening avenues for the development of inhibitors that veer away from targeting ATP binding pockets. This recent understanding of the functional modalities of DDR1 opens the complexity of targeting this transmembrane protein as per its functional prominence in the respective disease and thus demands the development of specific novel therapeutics. The perspective gives a short overview of recent developments of DDR1 inhibitors with the aid of the latest technologies, future directions for therapeutic development, and possibility of combinational therapeutic treatments to completely disengage functions of DDR1.

Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient.

COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.

Chemical Probe-Based Nanopore Sequencing to Selectively Assess the RNA Modifications.

Nanopore direct RNA sequencing (dRNA-Seq) reads reveal RNA modifications through consistent error profiles specific to a modified nucleobase. However, a null data set is required to identify actual RNA modification-associated errors for distinguishing it from confounding highly intrinsic sequencing errors. Here, we reveal that inosine creates a signature mismatch error in dRNA-Seq reads and obviates the need for a null data set by harnessing the selective reactivity of acrylonitrile for validating the presence of actual inosine modifications. Selective reactivity of acrylonitrile toward inosine altered multiple dRNA-Seq parameters like signal intensity and trace value. We also deduced the stoichiometry of inosine modification through deviation in signal intensity and trace value using this chemical biology approach. Furthermore, we devised Nano ICE-Seq, a protocol to overcome the low coverage issue associated with direct RNA sequencing. Taken together, our chemical probe-based approach may facilitate the knockout-free detection of disease-associated RNA modifications in clinical scenarios.

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs.

According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.