Biology subjects

Rahman, M. S.

Publications and source records attributed to Rahman, M. S..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Quantifying Pathological Progression from Single-Cell Data

The surge in single-cell datasets and reference atlases has enabled the comparison of cell states across conditions, yet a gap persists in quantifying pathological shifts from healthy cell states. To address this gap, we introduce single-cell Pathological Shift Scoring (scPSS) which provides a statistical measure for how much a "query" cell from a diseased sample has been shifted away from a reference group of healthy cells. In scPSS, The distance of a query cell to its k-th nearest reference cell is considered as its pathological shift score. Euclidean distances in the top n principal component space of the gene expressions are used to measure distances between cells. The p-value of a query pathological shift score belonging to the null distribution of intra-reference cell shift scores provides a statistical significance measure of the query cell being in the reference cell group. This makes our method both simple and statistically rigorous. Comparative evaluations against a state-of-the-art contrastive variational inference model, modified for shift scores, demonstrate our methods accuracy and efficiency. Additionally, we have also shown that the aggregation of cell-level pathological scores from scPSS can be used to predict health conditions at the individual level.

genomics↗

bioRxiv2024

GraFusionNet: Integrating Node, Edge, and Semantic Features for Enhanced Graph Representations

Understanding complex graph-structured data is a cornerstone of modern research in fields like cheminformatics and bioinformatics, where molecules and biological systems are naturally represented as graphs. However, traditional graph neural networks (GNNs) often fall short by focusing mainly on node features while overlooking the rich information encoded in edges. To bridge this gap, we present GraFusionNet, a framework designed to integrate node, edge, and molecular-level semantic features for enhanced graph classification. By employing a dual-graph autoencoder, GraFusionNet transforms edges into nodes via a line graph conversion, enabling it to capture intricate relationships within the graph structure. Additionally, the incorporation of Chem-BERT embeddings introduces semantic molecular insights, creating a comprehensive feature representation that combines structural and contextual information. Our experiments on benchmark datasets, such as Tox21 and HIV, highlight GraFusionNets superior performance in tasks like toxicity prediction, significantly surpassing traditional models. By providing a holistic approach to graph data analysis, GraFusion-Net sets a new standard in leveraging multi-dimensional features for complex predictive tasks. CCS CONCEPTSO_LIComputing methodologies [->] Neural networks. C_LI ACM Reference FormatMd Toki Tahmid, Tanjeem Azwad Zaman, and Mohammad Saifur Rahman. 2018. GraFusionNet: Integrating Node, Edge, and Semantic Features for Enhanced Graph Representations. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (Conference acronym XX). ACM, New York, NY, USA, 9 pages. https://doi.org/XXXXXXX.XXXXXXX

molecular biology↗

bioRxiv2024

DeepRNA-Twist: Language Model guided RNA Torsion Angle Prediction with Attention-Inception Network

RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNAs structural complexity and flexibility, as it can adopt multiple conformations, with experimental techniques being costly and computational approaches struggling to capture the intricate sequence dependencies needed for accurate predictions. To address these challenges, we introduce DeepRNA-Twist, a novel deep learning framework designed to predict RNA torsion and pseudo-torsion angles directly from sequence. DeepRNA-Twist utilizes RNA language model embeddings, which provides rich, context-aware feature representations of RNA sequences. Additionally, it introduces 2A3IDC module (Attention Augmented Inception Inside Inception with Dilated CNN), combining inception networks with dilated convolutions and multi-head attention mechanism. The dilated convolutions capture long-range dependencies in the sequence without requiring a large number of parameters, while the multi-head attention mechanism enhances the models ability to focus on both local and global structural features simultaneously. DeepRNA-Twist was rigorously evaluated on benchmark datasets, including RNA-Puzzles, CASP-RNA, and SPOT-RNA-1D, and demonstrated significant improvements over existing methods, achieving state-of-the-art accuracy. Source code is available at https://github.com/abrarrahmanabir/DeepRNA-Twist

bioinformatics↗

bioRxiv2024

Unveiling Novel Hyperglycosylated Analog of Human Erythropoietin: A Comprehensive Computational Exploration

IntroductionThe capability of human erythropoietin (hEPO) to treat anemia by stimulating erythropoiesis, has made it become one of the worlds leading biopharmaceuticals. An effective way to improve the efficiency of recombinant hEPO drugs is increasing glycosylation which can increase its serum half-life and thereby its in vivo biological activity. In this context, the objective of the present study was to explore potential glycosylation sites in hEPO to develop new hyperglycosylated analogous drug. Materials and MethodsA rational computational strategy was executed to select compatible region of hEPO for inserting N-linked glycosylation consensus motif (N-X-S/T), thus designing a number of analogs. The 3D models of the analogs were constructed by homology modeling and validated by various in silico tools. The probability of glycosylation was checked and hyperglycosylated models of the selected analogs were developed. Molecular docking and molecular dynamics simulation were performed to select the best analog. ResultsAmong 40 analogs designed in this study, Analog-71.1 showed most promising results. Four possible sites of this analog showed good probability for glycosylation. According to the molecular docking study, the analog obtained high docking score (2313.711) and formed highest number of (25) hydrogen bonds with erythropoietin receptors, which indicated strong and stable interaction with the receptors. Moreover, the steady trajectory of RMSD, Rgyr, RMSF and other findings obtained from MD simulation confirmed the structural stability of the analog. ConclusionsOur overall study has selected Analog-71.1 as a potential candidate to develop new analogous drug with enhanced efficiency, which needs further experimental analyses.

bioinformatics↗

bioRxiv2024

Mapping the PTEN Mutation Landscape: Structural and Functional Drivers of Lung Cancer

Lung cancer is the predominant form of cancer globally, arising from the dysfunction of genetic mutations. Although PTEN mutation is crucial in the aetiology of lung cancer, the mapping of these major drivers has to be determined. We leverage computational algorithms on 43,855 SNPs of PTEN to discover the mutational impact contributing to lung cancer. Fifteen variations were identified as detrimental, and no pertinent studies have previously addressed their structural and functional aspects. Notably, seven variations were identified as the most significant contributors to lethal effects in functional aberration, as demonstrated by the computational assessment. Subsequently, molecular simulation elucidated the structural instability associated with these alterations. Furthermore, drug binding experiments at the mutational site corroborated the destabilization experiments by demonstrating the conformational alteration of the structure, resulting in varied amino acid interactions. In summary, the present study elucidates the influence of mutations in PTEN structure on its functional architecture.

cancer biology↗

bioRxiv2024

LOCAS: Multi-label mRNA Localization with Supervised Contrastive Learning

Traditional methods for mRNA subcellular localization often fail to account for multiple compartmentalization. Recent multi-label models have improved performance, but still face challenges in capturing complex localization patterns. We introduce LOCAS (Localization with Supervised Contrastive Learning), which integrates an RNA language model to generate initial embeddings, employs supervised contrastive learning (SCL) to identify distinct RNA clusters, and uses a multi-label classification head (ML-Decoder) with cross-attention for accurate predictions. Through extensive ablation studies and multi-label overlapping threshold tuning, LOCAS achieves state-of-the-art performance across all metrics, providing a robust solution for RNA localization tasks.

bioinformatics↗

bioRxiv2024

Rickettsia rickettsii encodes a secretory lipase that facilitates intracytosolic colonization in host cells.

The key cellular processes required for rickettsial obligate intracellular lifestyle, include internalization by phagocytosis, regulation of intracellular trafficking, and evasion of lysosomal destruction to establish an intracytosolic replication niche, remain poorly defined. Recent reports showed that rickettsial phospholipases play an important role in vacuolar escape, but their functions are dispensable depending on the host cell-type. Here, we report the identification of a highly conserved putative lipase containing a Serine hydrolase motif (GXSXG), named RLip (Rickettsia Lipase). Our work reveals that RLip expression is cytotoxic to yeast cells, a genetically tractable heterologous model system. We demonstrate that RLip possesses lipase enzymatic activity and show a lipid specificity towards phosphoinositide (PI)(3), PI(3,4,5)P3, and PI(3,4)P2, and to a lesser extent PI(4,5)P2. Further, we found that RLip expression is induced during infection of pathogenic R. rickettsii, while its expression is low or undetectable for R. parkeri (mild-pathogenic) and R. montanensis (non-pathogenic), respectively, during host invasion. Intriguingly, RLip is highly enriched in the cytoplasmic fraction of host cells, however, minimally retained by the rickettsiae themselves, suggesting RLip is synthesized during infection and then secreted into the host cell cytoplasm. Neutralization of RLip activity, by antibody-blocking, significantly abrogated R. rickettsii escape from bactericidal phagolysosomal fusion, suggesting RLip plays a critical role in facilitating the intracytosolic colonization of pathogenic R. rickettsii. ImportanceArthropod-borne rickettsial diseases are on the rise globally, presenting a perilous threat to humans and livestock. However, our inadequate understanding on how Rickettsia manipulates cellular processes, including the evasion of lysosomal destruction, has impaired the development of effective therapeutic interventions. Here, we identify of a conserved putative lipase containing a Serine hydrolase motif, named RLip (Rickettsia Lipase). Our work demonstrates that RLip possesses lipase enzymatic activity and is enriched in the cytoplasm of host cells, while minimally retained by the bacteria itself. Neutralization of RLip activity abrogated R. rickettsii escape from bactericidal phagolysosomal fusion. In sum, our data support a mechanism by which pathogenic R. rickettsii employs RLip to escape from bactericidal phagolysosomal fusion in order to colonize the host.

microbiology↗

bioRxiv2024

Optimizing Plant Biofactories: Enhancing Recombinant Protein Production in Nicotiana benthamiana through Phytoplasma Effectors

Molecular farming, which utilizes plants as biofactories for recombinant protein production, offers an innovative and cost-effective alternative to traditional expression systems. Despite its advantages, plant-based production faces challenges such as low transgene expression and protein instability. Recent studies have highlighted the potential of Nicotiana benthamiana axillary stem leaves to enhance protein yield. This study explored the development of N. benthamiana lines expressing TENGU without signal peptide (T-SP), a phytoplasma effector known to induce plant dwarfism and increase shoot growth. TENGU and other effectors, such as SAP05 and SAP11, were introduced to create phenotypic variations that favor recombinant protein production. This study aimed to optimize these transgenic lines for increased biomass and protein yields by leveraging vertical farming conditions for scalable production. The results demonstrated significant improvements in leaf number, biomass, and five times more soluble protein content in T-SP lines compared to control lines, suggesting a promising approach for efficient molecular farming.

synthetic biology↗

bioRxiv2024

Polymeric Micellar Nanoparticles Enable Image-guided Drug Delivery in Solid Tumors

We report the development of a nanotechnology to co-deliver chemocoxib A with a reactive oxygen species (ROS)-activatable and COX-2 targeted pro-fluorescent probe, fluorocoxib Q (FQ) enabling real time visualization of COX-2 and CA drug delivery into solid cancers, using a di-block PPS135-b-POEGA17 copolymer, selected for its intrinsic responsiveness to elevated reactive oxygen species (ROS), a key trait of the tumor microenvironment. FQ and CA were synthesized independently, then co-encapsulated within micellar PPS135-b-POEGA17 co-polymeric nanoparticles (FQ-CA-NPs), and were assessed for cargo concentration, hydrodynamic diameter, zeta potential, and ROS-dependent cargo release. The uptake of FQ-CA-NPs in mouse mammary cancer cells and cargo release was assessed by fluorescence microscopy. Intravenous delivery of FQ-CA-NPs to mice harboring orthotopic mammary tumors, followed by vital optimal imaging, was used to assess delivery to tumors in vivo. The CA-FQ-NPs exhibited a hydrodynamic diameter of 109.2 {+/-} 4.1 nm and a zeta potential ({zeta}) of -1.59 {+/-} 0.3 mV. Fluorescence microscopy showed ROS-dependent cargo release by FQ-CA-NPs in 4T1 cells, decreasing growth of 4T1 breast cancer cells, but not affecting growth of primary human mammary epithelial cells (HMECs). NP-derived fluorescence was detected in mammary tumors, but not in healthy organs. Tumor LC-MS/MS analysis identified both CA (2.38 nmol/g tumor tissue) and FQ (0.115 nmol/g tumor tissue), confirming the FQ-mediated image guidance of CA delivery in solid tumors. Thus, co-encapsulation of FQ and CA into micellar nanoparticles (FQ-CA-NPs) enabled ROS-sensitive drug release and COX-2-targeted visualization of solid tumors.

bioengineering↗

bioRxiv2024

Pathogenic rickettsiae utilize the phosphatidylserine binding receptor CD300f on macrophages for host invasion and pathogenesis.

Some arthropod-borne obligate intracellular rickettsiae are among the most virulent human pathogens. Rickettsia species modulate immune (e.g., macrophages; M{Phi}) and non-immune cell (e.g., endothelial cells) responses to create a habitable environment for host colonization. In particular, M{Phi} play a crucial role in either terminating an infection at an early stage or succumbing to bacterial replication and colonization. However, our understanding on how Rickettsia species invade host cells, including M{Phi}, remain poorly defined. In this study, we describe a mechanism of host invasion by Rickettsia species, involving rickettsial phosphatidylserine (PS), as a ligand, and the CD300f receptor on M{Phi}. Using bone marrow-derived macrophages (BMDM{Phi}) from wild-type (WT) and CD300f-/- mice, we demonstrated that engulfment of both pathogenic R. typhi (the etiologic agent of murine typhus) and R. rickettsii (the etiologic agent of Rocky Mountain spotted fever) species as well as the non-pathogenic R. montanensis was significantly reduced in CD300f-/- BMDM{Phi} as compared to that of WT BMDM{Phi}. Furthermore, our mechanistic analysis suggests bacterial PS as the potential source for the CD300f-mediated rickettsiae engulfment by M{Phi}. In vivo infection studies using WT and CD300f-/- C57BL/6J mice showed that CD300f-/- animals were protected against R. typhi-or R. rickettsii-induced fatal rickettsiosis, which correlated with levels of bacterial burden detected in the spleens of mice. Adoptive transfer studies further revealed that CD300f-expressing M{Phi} are important mediators to control rickettsiosis in vivo. Collectively, our findings describe a previously unappreciated role for the efferocytic receptor, CD300f, to facilitate engulfment of rickettsiae within the host.

microbiology↗

bioRxiv2024

Prediction of protein-carbohydrate binding sites from protein primary sequence

BackgroundA protein is a large complex macromolecule that has a crucial role in performing most of the work in cells and tissues. It is made up of one or more long chains of amino acid residues. Another important biomolecule, after DNA and protein, is carbohydrate. Carbohydrates interact with proteins to run various biological processes. Several biochemical experiments exist to learn the protein-carbohydrate interactions, but they are expensive, time-consuming, and challenging. Therefore, developing computational techniques for effectively predicting protein-carbohydrate binding interactions from protein primary sequence has given rise to a prominent new field of research. ResultIn this study, we propose StackCBEmbed, an ensemble machine learning model to effectively classify protein-carbohydrate binding interactions at residue level. StackCBEmbed combines traditional sequence-based features along with features derived from a pre-trained transformer-based protein language model. To the best of our knowledge, ours is the first attempt to apply protein language model in predicting protein-carbohydrate binding interactions. StackCBEmbed achieved sensitivity and balanced accuracy scores of 0.730, 0.776 and 0.666, 0.742 in two separate independent test sets. This performance is superior compared to the earlier prediction models benchmarked in the same datasets. ConclusionWe thus hope that StackCBEmbed will discover novel protein-carbohydrate interactions and help advance the related fields of research. StackCBEmbed is freely available as Python scripts at https://github.com/nafiislam/StackCBEmbed.

bioinformatics↗

bioRxiv2023

CC-Tempo: A cell-cell communication aware temporal model of cellular dynamics

Delineating the mechanisms underlying cell state changes is key to gaining insights into organismal development and disease prognosis. Various methods have been proposed to study cellular differentiation and cell fate specification. However, they either do not incorporate temporal information or do not consider the vital role of intercellular communication in cellular differentiation and cell fate determination. Furthermore, many of these methods lack interpretability, making it difficult to identify the critical genes and pathways that influence the differentiation process. Here we propose CC-Tempo, a cell-cell communication-aware model of cellular dynamics that leverages intercellular communication scores and can help identify important genes and pathways crucial for different stages of differentiation in various lineages. While previous studies have indicated that scRNA-seq data alone may not suffice for accurately predicting cell fates, CC-Tempo demonstrates that incorporating intercellular communication significantly enhances the performance of such models. CC-Tempo can predict the significance of genes and pathways at different stages of the differentiation process. By perturbing these genes in silico, CC-Tempo reveals their efficacy for manipulating cell fate, which can be crucial for defining efficient reprogramming factors.

developmental biology↗

bioRxiv2023

PRIEST - Predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information

The dynamic evolution of the SARS-CoV-2 virus is largely driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate prediction of such mutations is fundamental in mitigating pandemic spread and developing effective control measures. In this study, we introduce a robust and interpretable deep-learning approach called PRIEST. This innovative model leverages time-series viral sequences to foresee potential viral mutations. Our comprehensive experimental evaluations underscore PRIESTs proficiency in accurately predicting immune-evading mutations. Our work represents a substantial step forward in the utilization of deep-learning methodologies for anticipatory viral mutation analysis and pandemic response.

bioinformatics↗

bioRxiv2023

CRISPR-DIPOFF: An Interpretable Deep LearningApproach for CRISPR Cas-9 Off-Target Prediction

CRISPR Cas-9 is a groundbreaking gene-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture, and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network (RNN) and transformer based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models performance. The results from our experiments demonstrate significant performance improvement compared to the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of sgRNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability, and a desirable balance between precision and recall.

bioinformatics↗

bioRxiv2023

Phylogenetic diversity and functional potential of large and cell-associated viruses in the Bay of Bengal

The Bay of Bengal (BoB), the largest bay in the world, provides valuable ecosystem services such as fishing and recreation to millions of people living along its coast and has a significant economic value. The BoB is impacted by various environmental factors such as seasonal monsoons and multiple freshwater inputs, and this region is particularly vulnerable to sea-level rise and increased frequency of devastating cyclones that are predicted to be exacerbated due to global climate change. These factors are also compounded by anthropogenic influences from tourism and development, making it an important ecosystem to understand and study from a global change perspective. Despite its importance, microbial diversity and ecology have remained largely understudied in this region. In this study, we describe the diversity and putative functional importance of large and cell-associated (that is, originating from the cellular size fraction) viruses from two coastal sites in the BoB, with an emphasis on giant viruses and large phages. Sites chosen for this study include Coxs Bazar, a populated beach with multiple freshwater inputs, and Saint Martin Island, a resort island that has considerably less human influence compared to Coxs Bazar. Through metagenomic sequencing, we were able to identify a more abundant and more diverse viral community at Coxs Bazar consisting of many viruses that are indicators of freshwater intrusion and runoff. Overall, 1962 putative phage genome bins were obtained ranging from 10 - 655 kilobase pairs (kbp) in sizes. Of these genomes, 16 from Saint Martin were found to be larger than 100kbp which we deemed "large" phages, and we were able to reconstruct a phylogeny of these large phages using the TerL gene as a marker. This phylogeny revealed clades enriched in large phages and a high diversity of large phage candidates in the Bay of Bengal coast. Protein annotation analysis showed a wide variety of functionality from both sites with more auxiliary metabolic genes (AMGs) found in the Coxs Bazar viral community. Five giant virus (Phylum Nucleocytoviricota) genomes were also reconstructed from Coxs Bazar and identified as belonging to the orders Imitervirales and Pandoravirales. These genomes ranged from 83 - 876 kbp in size and contained a wide range of encoded functionalities. To the best of our knowledge, our study represents the first insights on the phylogenetic and functional diversity of viruses in the Bay of Bengal. These results thus provide an important foundation for further studies on the impact of host-virus interactions on biogeochemical cycles and microbial food web in this understudied marine environment.

microbiology↗

bioRxiv2023

Exploring Microbial Diversity and Functional Potential along the Bay of Bengal Coastline in Bangladesh: Insights from Amplicon Sequencing and Shotgun Metagenomics

Although the Bay of Bengal (BoB) is the worlds largest bay, possessing distinct physiochemical properties, it has garnered little research focus concerning its microbial diversity and ecological importance. Here, we present amplicon (16S and 18S) profiling and shotgun metagenomics data regarding microbial communities from BoBs eastern coast, viz., Saint Martin and Coxs Bazar, Bangladesh. From the 16S sequencing data, Proteobacteria appeared to be the dominant phylum in both locations, with Alteromonas, Methylophaga, Anaerospora, Marivita, and Vibrio dominating in Coxs Bazar and Pseudoalteromonas, Nautella, Marinomonas, Vibrio, and Alteromonas dominating the Saint Martin site. From the 18S sequencing data, Ochrophyta, Chlorophyta, and Protalveolata appeared among the most abundant eukaryotic divisions in both locations, with significantly higher abundance of Choanoflagellida, Florideophycidae, and Dinoflagellata in Coxs Bazar. Functional annotations revealed that the microbial communities in these samples harbor genes for biofilm formation, quorum sensing, xenobiotics degradation, antimicrobial resistance, and a variety of other processes. Together, these results provide the first molecular insight into the functional and phylogenetic diversity of microbes along the BoB coast of Bangladesh and lay the foundation for further in-depth assessment of microbial community dynamics and functional potential in the context of global change in this region.

microbiology↗

bioRxiv2022

SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction

MotivationProtein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ({phi} and{psi} ) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles. ResultsWe present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, CAMEO, and CASP. The experimental results suggest that our proposed self-attention based network, together with transfer learning, has achieved notable improvements over the best alternate methods. Availability and implementationSAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Contactshams_bayzid@cse.buet.ac.bd Supplementary informationSupplementary material SM.pdf.

bioinformatics↗

bioRxiv2022

Comparative Phylogenetic Analysis and Transcriptomic Profiling of Dengue (DENV-3 genotype I) Outbreak in 2021 in Bangladesh

ObjectivesThe lack of a dengue disease animal model and the complex immune interaction in dengue infection hinders the study of host response and immunopathogenesis. The development of next-generation sequencing (NGS) technology allowed the researchers to study the transcriptomic profiles of human in-depth. We, therefore, implicated phylodynamic and transcriptomic approaches through NGS technology to know the origin of dengue virus (DENV) and their host response in infected patients with dengue fever. MethodsTo determine the whole genome sequences of the dengue virus and their transcriptomic profiles, RNA was extracted from the serum samples of 3 healthy, and 21 dengue patients. These samples were custom performed at phylogenetic, phylodynamic, differential express gene (DEG), and gene ontology (GO) using respective bioinformatics tools. ResultsThe whole genome sequence analysis revealed that the total number of nucleotide ranges on these serum RNA samples were in between 10647 and 10707. Phylogenetic tree analysis showed that these strains were belonged to DENV-3 genotype I. Phylodynamic analysis showed that the 2021 epidemic isolates were clade shifted and maintained as a new clade in compared to 2019 epidemic. Transcriptome analysis mapped a total of 19267 expressed genes. Of them, there were higher expression of genes in dengue-positive samples (n = 17375) with a count of 6953 unique genes in comparison to healthy controls (n = 12314) with a count of 1892 unique genes. A total of 2686 DEGs were identified in a host factor-independent manner in dengue patients with a q-value < 0.05. DESeq2 plot counts function of the top 24 genes with the smallest q-values of differential gene expression of RNA-seq data showed that 11 genes were upregulated, whereas 13 genes were downregulated. GO analysis showed a significant upregulation (p = <0.001) in a process of multicellular organismal, nervous system, sensory perception of chemical stimulus, and G protein-coupled receptor signalling pathways in the dengue patients. However, there were a significant downregulation (p = < 0.001) of intracellular component, cellular anatomical entity, and protein-containing complex in dengue patients. Most importantly, there was significant increase of a classes of immunity protein (Cytokines especially TGF-{beta}1, chemokines, inflammasome, and factors for blood coagulations) in dengue patients in compared to the healthy controls, with increased GO of immune system process. In addition, upregulation of toll receptor (TLR) signalling pathways were also initiated in the patients infected with dengue virus. These TLR pathways were particularly involved for the activation of innate in couple with adaptive immune system that probably involved the rapid elimination of dengue virus infected cells. These differentially expressed genes could be further investigated for target based prophylactic interventions for dengue. ConclusionThis is a first report to document the complete genomic features of dengue, and differentially expressed genes in patients with dengue virus in Bangladesh. These genes may have diagnostic and therapeutic values for dengue infection. Continual genomic surveillance is required to further investigate the shift in dominant genotypes in relation to viral pathogenesis.

evolutionary biology↗