Mass Spectrometric and Artificial Intelligence-Based Identification of the Secretome of Plasmodium falciparum Merozoites to Provide Novel Candidates for Vaccine Development Pipeline.

PURPOSE: Merozoites are the only extracellular form of blood stage parasites, making it a worthwhile target. Multiple invasins that are stored in the merozoite apical organelles, are secreted just prior to invasion, and mediates its interaction with RBC. A comprehensive identification of all these secreted invasins is lacking and this study addresses that gap. EXPERIMENTAL DESIGN: Pf3D7 merozoites were enriched and triggered to discharge apical organelle contents by exposure to ionic conditions mimicking that of blood plasma. The secreted proteins were separated from cellular contents and both the fractions were subjected to proteomic analysis. Also, the identified secreted proteins were subjected to GO, PPI network analysis, and AI-based in silico approach to understand their vaccine candidacy. RESULTS: A total of 63 proteins were identified in the secretory fraction with membrane and apical organellar localization. This includes various MSPs, micronemal EBAs and rhoptry bulb proteins, which play a crucial role in initial and late merozoite attachment, and majority of them qualified as vaccine candidates. CONCLUSION AND CLINICAL RELEVANCE: We, for the first time, report the secretory repertoire of merozoite and its status for vaccine candidacy. This information can be utilized to develop better invasion blocking multisubunit vaccines, comprising of immunological epitopes from several secreted invasins.

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data.

Background: The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. Methods: The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). Findings: AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. Interpretation: The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. Funding: This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.

DNASCANNER v2: A Web-Based Tool to Analyze the Characteristic Properties of Nucleotide Sequences.

Throughout the process of evolution, DNA undergoes the accumulation of distinct mutations, which can often result in highly organized patterns that serve various essential biological functions. These patterns encompass various genomic elements and provide valuable insights into the regulatory and functional aspects of DNA. The physicochemical, mechanical, thermodynamic, and structural properties of DNA sequences play a crucial role in the formation of specific patterns. These properties contribute to the three-dimensional structure of DNA and influence their interactions with proteins, regulatory elements, and other molecules. In this study, we introduce DNASCANNER v2, an advanced version of our previously published algorithm DNASCANNER for analyzing DNA properties. The current tool is built using the FLASK framework in Python language. Featuring a user-friendly interface tailored for nonspecialized researchers, it offers an extensive analysis of 158 DNA properties, including mono/di/trinucleotide frequencies, structural, physicochemical, thermodynamics, and mechanical properties of DNA sequences. The tool provides downloadable results and offers interactive plots for easy interpretation and comparison between different features. We also demonstrate the utility of DNASCANNER v2 in analyzing splice-site junctions, casposon insertion sequences, and transposon insertion sites (TIS) within the bacterial and human genomes, respectively. We also developed a deep learning module for the prediction of potential TIS in a given nucleotide sequence. In the future, we aim to optimize the performance of this prediction model through extensive training on larger data sets.