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Dhall, A.

Publications and source records attributed to Dhall, A..

At least 19 recordsLinked to original sources
bioRxiv2026

TR-107, a novel mitochondrial ClpP agonist, induces robust antitumor activity against preclinical models of adrenocortical carcinoma

AbstractAdrenocortical carcinoma (ACC) is a rare and highly aggressive endocrine malignancy originating from the adrenal cortex with limited effective treatment options. The underlying pathophysiology of ACC is uniquely characterized by abnormal steroid production and increased metabolic activity, highlighting the critical role of mitochondria in adrenal steroid hormone biosynthesis and tumor metabolism. In this study, we investigated the therapeutic potential of TR-107, a novel and highly selective small-molecule agonist targeting the mitochondrial protease ClpP. Pharmacologic hyperactivation of ClpP disrupts mitochondrial proteostasis and bioenergetics and has shown promising antitumor activity in various preclinical models. Our results demonstrated that TR-107 induces potent dose-dependent cytotoxic effects at nanomolar concentrations in ACC cell lines NCI-H295R and mACC3 as well as short-term ACC patient-derived organoid (PDO) models, markedly reducing cell viability and confluency in vitro. Metabolic analyses revealed that TR-107 significantly impaired oxygen consumption, indicating a disruption of oxidative phosphorylation and substantial attenuation of basal cellular respiration. Mechanistic studies showed dose-dependent increases in reactive oxygen species (ROS) levels and upregulation of proteins involved in mediating the ferroptotic rheostat. Pharmacokinetic assessment uncovered that TR-107 was not a substrate of the ABCB1 (MDR1/P-glycoprotein) efflux transporter, suggesting potential to overcome common multidrug resistance mechanisms. Given the importance of IGF-2 signaling in ACC, we further explored the combinatorial effects of TR-107 with IGF-1 receptor (IGF-1R) inhibitors and discovered that co-treatment produced synergistic reductions in cell viability across NCI-H295R, mACC3, and ACC PDOs. Collectively, these findings support the potential of mitochondrial ClpP hyperactivation as a promising therapeutic strategy for ACC and demonstrate that TR-107 exhibits significant antitumor activity as a monotherapy or in combination with IGF-1R inhibitors. These findings provide a strong rationale for advancing ClpP agonists into clinical development for the management of ACC.

cancer biology↗
bioRxiv2025

A Human Genetics Framework for De-risking Gene Editing Targets for Hematopoietic Cell and Gene Therapy

Developing novel therapeutics requires robust early-stage target de-risking to ensure safety and efficacy. We developed a scalable proteogenomic framework integrating population-scale human genetics and plasma proteomics to identify genes tolerant of inactivation (i.e., dispensable) within hematopoietic compartments, thereby enabling safer targeted immunotherapies. Using CD33 as a validated benchmark, we observed that naturally occurring loss-of-function (LoF) variants lead to concordant RNA and protein depletion, supporting functional gene inactivation. Early clinical results from the Trem-Cel trial (NCT05945849) further provide proof of concept that deletion of dispensable antigens can enable safe and effective immunotherapy in humans. We extended this approach genome-wide in the UK Biobank and identified 237 candidate dispensable genes, filtered by plasma proteomic data and hematopoietic expression, highlighting LY75 (CD205) as a novel candidate with strong proteogenomic evidence of LoF tolerance. This work establishes a generalizable, quantitative proteogenomic framework for systematic prioritization of dispensable gene targets for editing, providing a foundation for next-generation cell and gene therapies that minimize on-target, off-tumor toxicities.

genetics↗
bioRxiv2025

SCLC-TumorMiner: A Directly Accessible Genomics Resource for Precision Oncology: Big Data for Small Cells

Small cell lung cancer (SCLC) is among the most aggressive malignancies. Unlike many other cancers, it is not represented in The Cancer Genome Atlas, and available datasets are fragmented across institutions, disease stages, and treatment settings. RNA sequencing provides a powerful and cost-effective approach, but the high dimensionality of transcriptomic data and the heterogeneity of patient cohorts pose significant challenges. To address such challenges, we developed SCLC TumorMiner (https://discover.nci.nih.gov/SclcTumorMinerCDB/), which includes 50 tumor samples from relapse patients at the National Cancer Institute (NCI) and 154 samples from untreated patients at the University of Cologne and Tongji University. SCLC TumorMiner enables molecular classification, genomic pathway analyses, risk stratification, identification of predictive cell-surface biomarkers such as DLL3 or TROP2, and drug-response biomarkers such as SLFN11. SCLC TumorMiner illustrates profound differences between untreated and relapse patient samples. Additionally, "MyPatient", one of SCLC TumorMiners modules, is presented as a medical assistant application prototype.

cancer biology↗
bioRxiv2025

Lamin A/C Deficiency Drives Genomic Instability and Poor Survival in Small-Cell Lung Cancer through Increased R-loop Accumulation

Lamin A/C (LMNA), a key component of the nuclear envelope, is essential for maintaining nuclear integrity and genome organization [1]. While LMNA dysregulation has been implicated in genomic instability across cancer and aging, the underlying mechanisms remain poorly understood [2]. Here, we investigate LMNAs role in small-cell lung cancer (SCLC), a highly aggressive malignancy characterized by extreme genomic instability [3, 4]. We demonstrate that LMNA depletion promotes R-loop accumulation, transcription-replication conflicts, replication stress, DNA breaks, and micronuclei formation. Mechanistically, LMNA loss disrupts nuclear pore complex distribution, reducing phenylalanine-glycine (FG)-nucleoporin incorporation and impairing RNA export efficiency. Furthermore, we show that LMNA expression is epigenetically repressed by EZH2 during SCLC differentiation from neuroendocrine (NE) to non-NE states. Clinically, low LMNA levels correlate with significantly worse survival in SCLC patients. These findings uncover a novel role for LMNA in safeguarding genome integrity and shaping tumor heterogeneity, with broad implications for cancer and aging. Significance StatementLamin A/C, a key structural component of the nuclear envelope, is frequently lost or mutated in cancer, laminopathies, and aging-related disorders. Lamin A/C loss is associated with genomic instability, but the underlying mechanisms remain incompletely understood. We demonstrate that LMNA loss drives genomic instability by promoting R-loop accumulation through disrupted nuclear pore dynamics and impaired RNA export. These findings reveal a previously unrecognized link between LMNA loss, nuclear envelope dysfunction, and genome instability. Targeting this pathway could help mitigate genomic instability in aging and laminopathies, while leveraging R-loop accumulation may enhance the efficacy of DNA-damaging therapies in cancer.

cancer biology↗
bioRxiv2025

Bimodal genomic approach predicting Semaphorin 7A (SEMA7A) as prognostic biomarker in adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy with high mortality and poor prognosis. To elucidate the genetic underpinnings of ACCs, we have analyzed the transcriptome data of 112 ACC tumor samples from patients enrolled in the TCGA and NCI. Among 72 bimodally expressed genes stratifying patients into prognostic groups, we focused on SEMA7A, as it encodes a glycosylphosphatidylinositol-anchored membrane glycoprotein (Semaphorin 7a) regulating integrin-mediated signaling, cell migration and immune responses. We find that high SEMA7A gene expression is associated with poor prognosis (hazard ratio = 4.27; p-value < 0.001). In hormone-producing ACCs, SEMA7A expression is elevated and positively correlated with genes driving steroidogenesis, aldosterone and cortisol synthesis, including CYP17A1, CYP11A1, INHA, DLK1, NR5A1 and MC2R. Correlation analyses show that SEMA7A is co-expressed with the integrin-{beta}1, FAK (focal adhesion kinase) and MAPK/ERK (mitogen-activated protein kinase/extracellular signal regulated kinases) signaling pathways. Immunohistochemistry (IHC) staining demonstrates the feasibility of evaluating SEMA7A in ACC tissues and shows significant correlation between gene expression (RNA-Seq) and protein expression (IHC). These findings suggest SEMA7A as a candidate for further research in ACC biology, a candidate for cancer therapy, as well as a potential prognosis biomarker for ACC patients. Translational relevanceAdrenocortical cancer (ACC) remains a challenging disease primarily due to the scarcity of reliable biomarkers for predicting patient outcomes and informing innovative therapeutic strategies, as well as its rarity, which restricts the scope of clinical trials. In our study, we performed RNAseq and IHC analyses of ACC samples sourced from The Cancer Genome Atlas (TCGA), tissue microarray slide, and National Cancer Institute (NCI) cancer patient samples. Our findings indicate that a substantial proportion of ACC tumors exhibit expression of SEMA7A, a glycoprotein involved in Semaphorin cell surface signaling. Notably, elevated levels of SEMA7A were identified as a poor prognostic biomarker and were associated with activation of the integrin-ERK-MAPK kinase signaling pathways. These results suggest that ACC tumors with high SEMA7A expression should be considered at elevated risk, and SEMA7A may serve as a potential target for immunotherapeutic strategies, including antibody-drug conjugates, T-cell engagers, and/or small molecule inhibitors targeting the MAPK pathway.

cancer biology↗
bioRxiv2025

Topoisomerase IIIb protects from tumorigenesis and immune dysregulation

Topoisomerase III-beta (Top3b) reduces nucleic acid torsional stress and intertwining generated during RNA and DNA metabolism while protecting the genome from pathological R-loops, which otherwise result in DNA breakage and genome instability. By studying Top3b knockout mice (Top3b-KO), we find that the loss of Top3b accelerates the development of spontaneous lymphoid tumors arising in spleens and lymph nodes, the organs with prominent Top3b expression. Aging Top3b-KO mice also display splenomegaly and systemic immune alterations including neutrophilia and lymphopenia suggestive of chronic inflammation. At the molecular level, Top3b deficiency causes genome-wide R-loop accumulation in splenocytes as measured by CUT&Tag sequencing. Increased R-loops is associated with genomic DNA breaks and activation of immune signaling pathways including the IL-6 signaling, interleukin-7 signaling and cGAS-STING. Moreover, knocking-out Top3b promotes the rapid development of syngeneic EL4 T-cell lymphomas. In conclusion, our work implies that, in addition to its role in preserving the nervous system, Top3b protects from tumorigenesis and immune dysregulations. SignificanceTopoisomerase III-beta (TOP3B) is the only dual RNA-DNA topoisomerase. Its inactivation has previously been shown to be critical for neurodevelopmental disorders in humans and R-loop suppression in cell line models. Here, we demonstrate that loss of Top3b accelerates spontaneous lymphomagenesis and promotes syngeneic lymphoma growth in association with innate immune defects and R-loop accumulation in Top3b-KO mice. These findings provide evidence for a previously unappreciated role of Top3b as a tumor suppressor gene and immune regulator.

cancer biology↗
bioRxiv2024

Identification of DLK1, a Notch ligand, as an immunotherapeutic target and regulator of tumor cell plasticity and chemoresistance in adrenocortical carcinoma

Immunotherapeutic targeting of cell surface proteins is an increasingly effective cancer therapy. However, given the limited number of current targets, the identification of new surface proteins, particularly those with biological importance, is critical. Here, we uncover delta-like non-canonical Notch ligand 1 (DLK1) as a cell surface protein with limited normal tissue expression and high expression in multiple refractory adult metastatic cancers including small cell lung cancer (SCLC) and adrenocortical carcinoma (ACC), a rare cancer with few effective therapies. In ACC, ADCT-701, a DLK1 targeting antibody-drug conjugate (ADC), shows potent in vitro activity among established cell lines and a new cohort of patient-derived organoids as well as robust in vivo anti-tumor responses in cell line-derived and patient-derived xenografts. However, ADCT-701 efficacy is overall limited in ACC due to high expression and activity of the drug efflux protein ABCB1 (MDR1, P-glycoprotein). In contrast, ADCT-701 is extremely potent and induces complete responses in DLK1+ ACC and SCLC in vivo models with low or no ABCB1 expression. Genetic deletion of DLK1 in ACC dramatically downregulates ABCB1 and increases ADC payload and chemotherapy sensitivity through NOTCH1-mediated adrenocortical de-differentiation. Single cell RNA-seq of ACC metastatic tumors reveals significantly decreased adrenocortical differentiation in DLK low or negative cells compared to DLK1 positive cells. This works identifies DLK1 as a novel immunotherapeutic target that regulates tumor cell plasticity and chemoresistance in ACC. Our data support targeting DLK1 with an ADC in ACC and neuroendocrine neoplasms in an active first-in-human phase I clinical trial (NCT06041516).

cancer biology↗
bioRxiv2024

Metastatic organotropism in small cell lung cancer

Metastasis is the leading cause of cancer-related deaths, yet its regulatory mechanisms are not fully understood. Small-cell lung cancer (SCLC) is the most metastatic form of lung cancer, with most patients presenting with widespread disease, making it an ideal model for studying metastasis. However, the lack of suitable preclinical models has limited such studies. We utilized rapid autopsy-derived tumors to develop xenograft models that mimic key features of SCLC, including histopathology, rapid and widespread development of metastasis to the liver, brain, adrenal, bone marrow, and kidneys within weeks, and response to chemotherapy. By integrating in vivo lineage selection with comprehensive bulk and single cell multiomic profiling of transcriptomes and chromatin accessibility, we identified critical cellular programs driving metastatic organotropism to the liver and brain, the most common sites of SCLC metastasis. Our findings reveal the key role of nuclear-cytoskeletal interactions in SCLC liver metastasis. Specifically, the loss of the nuclear envelope protein lamin A/C, encoded by the LMNA gene, increased nuclear deformability and significantly increased the incidence of liver metastasis. Human liver metastases exhibited reduced LMNA expression compared to other metastatic sites, correlating with poorer patient outcomes and increased mortality. This study introduces novel preclinical models for SCLC metastasis and highlights pathways critical for organ-specific metastasis, offering new avenues for the development of targeted therapies to prevent or treat metastatic disease.

cancer biology↗
bioRxiv2024

Mitochondrial topoisomerase I (Top1MT) prevents the onset of metabolic dysfunction-associated steatohepatitis (MASH) in mice

High fat (HF) diet is a major factor in the development of metabolic dysfunction-associated steatotic liver disease (MASLD) and steatohepatis (MASH), and mitochondria have been proposed to play a role in the pathogenesis of HF diet-induced MASH. Because Mitochondrial topoisomerase I (Top1MT) is exclusively present in mitochondria and Top1MT knock-out mice are viable, we were able to assess the role of Top1MT in the development of MASH. We show that after 16 weeks of HF diet, mice lacking Top1MT are prone to the development of severe MASH characterized by liver steatosis, lobular inflammation and hepatocyte damage. Mice lacking Top1MT also show prominent mitochondrial dysfunction, ROS production and mitochondrial DNA (mtDNA) release, accompanied by hepatic inflammation and fibrosis. In summary, our study demonstrates the importance of Top1MT in sustaining hepatocyte functions and suppressing MASH.

molecular biology↗
bioRxiv2024

Genome-wide Mapping of Topoisomerase Binding Sites Suggests Topoisomerase 3α(TOP3A) as a Reader of Transcription-Replication Conflicts (TRC)

Both transcription and replication can take place simultaneously on the same DNA template, potentially leading to transcription-replication conflicts (TRCs) and topological problems. Here we asked which topoisomerase(s) is/are the best candidate(s) for sensing TRC. Genome-wide topoisomerase binding sites were mapped in parallel for all the nuclear topoisomerases (TOP1, TOP2A, TOP2B, TOP3A and TOP3B). To increase the signal to noise ratio (SNR), we used ectopic expression of those topoisomerases in H293 cells followed by a modified CUT&Tag method. Although each topoisomerase showed distinct binding patterns, all topoisomerase binding signals positively correlated with gene transcription. TOP3A binding signals were suppressed by DNA replication inhibition. This was also observed but to a lesser extent for TOP2A and TOP2B. Hence, we propose the involvement of TOP3A in sensing both head-on TRCs (HO-TRCs) and codirectional TRCs (CD-TRCs). In which case, the TOP3A signals appear concentrated within the promoters and first 20 kb regions of the 5 -end of genes, suggesting the prevalence of TRCs and the recruitment of TOP3A in the 5-regions of transcribed and replicated genes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/599352v1_ufig1.gif" ALT="Figure 1"> View larger version (10K): org.highwire.dtl.DTLVardef@1d1afdeorg.highwire.dtl.DTLVardef@17146eaorg.highwire.dtl.DTLVardef@1e6bed3org.highwire.dtl.DTLVardef@1fa40db_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗
bioRxiv2023

A Deep Learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature. In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Additionally, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. The models developed in this study have been trained on 80% of the GSE181919 dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV-patients on the validation set. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes and found that most genes were involved in binding and catalytic activities. To facilitate the scientific community, a software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/. Key PointsO_LIApplication of single cell transcriptomics in cancer diagnosis C_LIO_LIDevelopment of models for predicting HNSCC patients C_LIO_LIClassification of HPV+ and HPV-HNSCC patients C_LIO_LIIdentification of gene biomarkers from single cell sequencing C_LIO_LIA standalone software package HNSCpred for predicting HNSCC patients C_LI Authors BiographyO_LIAkanksha Jarwal is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAman Srivastava is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as a Professor and Head of the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

HLA-DR4Pred2: an improved method for predicting HLA-DRB1*04:01 binders

HLA-DRB1*04:01 is associated with many disease that include sclerosis, arthritis, diabetes and Covid19. Thus, it is important to scan binders of HLA-DRB1*04:01 in an antigen to develop immunotherapy, vaccine and protection against these diseases. One of the major limitations of existing methods for predicting with HLA-DRB1*04:01 binders is that these methods trained on small datasets. This study present a method HLA-DR4Pred2 developed on a large dataset contain 12676 binders and equal number of non-binders. It is an improved version of HLA-DR4Pred, which was trained on a small dataset contain only 576 binders and equal number of binders. All models in this study were trained, optimized and tested on 80% of data called training datasets using five-fold cross-validation; final models were evaluated on 20% of data called validation/independent dataset. A wide range of machine learning techniques have been employed to develop prediction models and achieved maximum AUC of 0.90 and 0.87 on validation dataset using composition and binary profile features respectively. The performance of our composition based model increased from 0.90 to 0.93 when combined with BLAST search. In addition, we also developed our models on alternate or realistic dataset that contain 12676 binders and 86300 non-binders and achieved maximum AUC 0.99. Our method perform better than existing methods when we compare the performance of our best model with performance of existing methods on validation dataset. Finally, we developed standalone and online version of HLA-DR4Pred2 for predicting, designing and virtual scanning of HLA- DRB1*04:01(https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2). Key PointsO_LIHLADR4Pred2.0 is an update of HLADR4Pred C_LIO_LIPredict the binding or non-binding peptides for MHC-Class II allele HLA- DRB1*04:01 C_LIO_LIUsed alignment free and alignment based hybrid approach C_LIO_LIMotifs which are highly specific to HLA-DRB1*04:01 binders C_LIO_LIBenchmark the performance of the other existing methods with HLADR4Pred2.0 C_LI Authors BiographyO_LISumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria

This study focuses on the development of in silico models for predicting antibacterial peptides as a potential solution for combating antibiotic-resistant strains of bacteria. Existing methods for predicting antibacterial peptides are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we introduce a novel approach that enables the prediction of antibacterial peptides against several bacterial groups, including gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify antibacterial peptides and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict antibacterial peptides and obtained high precision with low sensitivity. To address the similarity issue, we developed machine learning-based models using a variety of compositional and binary features. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98 and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, when evaluated on a validation/independent dataset. Our attempts to develop hybrid or ensemble methods by merging machine learning models with similarity and motif-based techniques did not yield any improvements. To ensure robust evaluation, we employed standard techniques such as five-fold cross-validation, internal validation, and external validation. Our method performs better than existing methods when we compare our method with existing approaches on an independent dataset. In summary, this study makes significant contributions to the field of antibacterial peptide prediction by providing a comprehensive set of methods tailored to different bacterial groups. As part of our contribution, we have developed the AntiBP3 web server and standalone package, which will assist researchers in the discovery of novel antibacterial peptides for combating bacterial infections (https://webs.iiitd.edu.in/raghava/antibp3/). Key Points BLAST-based similarity for annotating antibacterial peptides. Machine learning-based models developed using composition and binary profiles. Identification and mapping of motifs exclusively found in antibacterial peptides Improved version of AntiBP and AntiBP2 for predicting antibacterial peptides. Web server for predicting/designing/scanning antibacterial peptides for all groups of bacteria Authors BiographyO_LINisha Bajiya is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

Prediction of Alzheimer's Disease from Single Cell Transcriptomics Using Deep Learning

Alzheimers disease (AD) is a progressive neurological disorder characterized by brain cell death, brain atrophy, and cognitive decline. Early diagnosis of AD remains a significant challenge in effectively managing this debilitating disease. In this study, we aimed to harness the potential of single-cell transcriptomics data from 12 Alzheimers patients and 9 normal controls (NC) to develop a predictive model for identifying AD patients. The dataset comprised gene expression profiles of 33,538 genes across 169,469 cells, with 90,713 cells belonging to AD patients and 78,783 cells belonging to NC individuals. Employing machine learning and deep learning techniques, we developed prediction models. Initially, we performed data processing to identify genes expressed in most cells. These genes were then ranked based on their ability to classify AD and NC groups. Subsequently, two sets of genes, consisting of 35 and 100 genes, respectively, were used to develop machine learning-based models. Although these models demonstrated high performance on the training dataset, their performance on the validation/independent dataset was notably poor, indicating potential overoptimization. To address this challenge, we developed a deep learning method utilizing dropout regularization technique. Our deep learning approach achieved an AUC of 0.75 and 0.84 on the validation dataset using the sets of 35 and 100 genes, respectively. Furthermore, we conducted gene ontology enrichment analysis on the selected genes to elucidate their biological roles and gain insights into the underlying mechanisms of Alzheimers disease. While this study presents a prototype method for predicting AD using single-cell genomics data, it is important to note that the limited size of the dataset represents a major limitation. To facilitate the scientific community, we have created a website to provide with code and service. It is freely available at https://webs.iiitd.edu.in/raghava/alzscpred. Key PointsO_LIPredictive Model for Alzheimers Disease Using Single Cell Transcriptomics Data C_LIO_LIOveroptimization of models trained on single-cell genomics data. C_LIO_LIApplication of dropout regularization technique of ANN for reducing overoptimization C_LIO_LIRanking of genes based on their ability to predict patients Alzheimers Disease C_LIO_LIStandalone software package for predicting Alzheimers Disease C_LI Authors BiographyO_LIAman Srivastava is pursuing M. Tech. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Jarwal is pursuing M. Tech. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

A host-specific technique has been developed for annotating interferon-gamma (IFN-{gamma}) inducing peptides, it is an updated version of IFNepitope. In this study, dataset used for developing prediction method contain experimentally validated 25492 and 7983 IFN-{gamma} inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. In order to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST and achieved AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-{gamma} inducing peptides, thus server/software have been developed (https://webs.iiitd.edu.in/raghava/ifnepitope2/). HighlightsO_LIAn updated method for predicting interferon-gamma (IFN-{gamma}) inducing peptides. C_LIO_LIA wide range of features have been generated using Pfeature tool. C_LIO_LIModels were trained and tested on experimentally validated datasets. C_LIO_LIHybrid models developed by combining machine-learning and BLAST. C_LIO_LIIFNepitope2 server is available to design subunit or peptide-based vaccines. C_LI

bioinformatics↗
bioRxiv2022

Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines

Advancements in cancer immunotherapy have shown significant outcomes in treating various types of cancers. In order to design effective immunotherapy, it is important to understand immune response of a patient based on its genomic profile. We compute prognostic biomarkers from 8346 cancer patients for twenty types of cancer. These prognostic biomarkers has been computed based on i) presence of 352 human leucocyte antigen class-I (HLA-I), ii) 660959 tumor-specific HLA1 neobinders and iii) expression profile of 153 cytokines. It was observed that survival risk of cancer patients depends on presence of certain type of HLA-I alleles; for example LIHC cancer patients with HLA-A*03:01 are on lower risk. Our analysis indicate that neobinders of HLA-I alleles have high correlation with overall survival of certain type of cancer patients. For example HLA-B*07:02 binders have 0.49 correlation with survival of LUSC and -0.77 with KICH cancer patients. It is clear from above analysis that HLA and their binders have major role in survival of cancer patients suffering from different types of cancer. In addition, we compute prognostic biomarkers for 20 types of cancer based on each type of cytokine expression. Higher expression of few cytokines is survival favourable like IL-2 for BLCA cancer patients whereas IL-5R survival unfavourable for KICH cancer patients. In order to facilitate scientific community we developed a web-based platform CancerHLA1 that maintain raw and analyzed data (https://webs.iiitd.edu.in/raghava/cancerhla1/).

cancer biology↗
bioRxiv2022

Prediction, scanning and designing of TNF-α inducing epitopes for human and mouse

Tumor Necrosis Factor alpha (TNF-) is a pleiotropic pro-inflammatory cytokine that plays a crucial role in controlling signaling pathways within the immune cells. Recent studies reported that the higher expression levels of TNF- is associated with the progression of several diseases including cancers, cytokine release syndrome in COVID-19 and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF- progression in various disease conditions. In the pilot study, we have proposed a host-specific in-silico tool for the prediction, designing and scanning of TNF- inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF- inducing/non-inducing for human and mouse hosts. Firstly, we developed alignment free (machine learning based models using composition of peptides) methods for predicting TNF- inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, alignment based (using BLAST) method has been used for predicting TNF- inducing epitopes. Finally, a hybrid method (combination of alignment free and alignment-based method) has been developed for predicting epitopes. Our hybrid method achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified the potential TNF- inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2 and human insulin. Best models developed in this study has been incorporated in a webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope). Key PointsO_LITNF- is a multifunctional pleiotropic pro-inflammatory cytokine. C_LIO_LIAnti-TNF- therapy used as an effective treatment in several autoimmune disorders. C_LIO_LIComposition-based features generated using Pfeature for each peptide sequence. C_LIO_LIAlignment-based and alignment-free models developed. C_LIO_LIPrediction and scanning of TNF- inducing regions in antigens. C_LIO_LITNFepitope is available as a web-server, standalone package and GitLab. C_LI

bioinformatics↗
bioRxiv2022

Prediction of celiac disease associated epitopes and motifs in a protein

Celiac disease (CD) is an autoimmune gastrointestinal disorder which causes immune-mediated enteropathy against gluten. The gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2 and HLA-DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. In addition, prediction of CD associated epitope/peptide is also required for developing antigen-based immunotherapy against celiac disease. In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used in this study for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated which achieve maximum AUROC 0.98. We discovered CD associated motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package "CDpred". We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/). Key PointsO_LICeliac disease is one of the prominent autoimmune diseases C_LIO_LIGluten immunogenic peptides are responsible for celiac disease C_LIO_LIMapping of celiac disease associated epitopes and motifs on a proteins C_LIO_LIIdentification of proline and glutamine rich regions C_LIO_LIA web server and software package for predicting CD associate peptides C_LI Authors BiographyO_LIRitu Tomer is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

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