Biology subjects

Das, J.

Publications and source records attributed to Das, J..

88 records · Page 5Linked to original sources

bioRxiv2021

DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types

BackgroundFlow cytometry is a classical approach used to define cell types in peripheral blood. While DNA methylation signatures have been extensively employed in recent years as an alternative to flow cytometry to define cell populations in peripheral blood, this approach has not been tested in lung-derived samples. Here, we compared bronchoalveolar lavage with a more cost-effective and less invasive technique based on sputum induction and developed a DNA methylome-based algorithm that can be used to deconvolute the cell types in such samples. ResultsWe analyzed the DNA methylome profiles of alveolar macrophages and lymphocytes cells isolated from the pulmonary compartment. The cells were isolated using two different methods, sputum induction and bronchoalveolar lavage. A strong positive correlation between the DNA methylome profiles of cells obtained with the two isolation methods was observed, and in two of the donors, in which the correlation was best, a later analyses demonstrated that those subjects the samples were consistently derived from the lower part of the lungs. We also identified unique patterns of CpG methylation in DNA obtained from the two cell populations, which can be used as a signature to discriminate between the alveolar macrophages and lymphocytes by means of open-source algorithms. We validated our findings with external data and obtained results consistent with the previous findings. ConclusionsOur analysis opens up a new possibility to identify different cell populations from lung samples and promotes sputum induction as a tool to study immune cell populations from the lung.

genomics↗

bioRxiv2021

Bifurcated monocyte states are predictive of mortality in severe COVID-19

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection presents with varied clinical manifestations1, ranging from mild symptoms to acute respiratory distress syndrome (ARDS) with high mortality2,3. Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in severe COVID-19. We performed high-dimensional cellular and molecular profiling of blood and respiratory samples from critically ill COVID-19 patients to define immune cell genomic states that are predictive of outcome in severe COVID-19 disease. Critically ill patients admitted to the intensive care unit (ICU) manifested increased frequencies of inflammatory monocytes and plasmablasts that were also associated with ARDS not due to COVID-19. Single-cell RNAseq (scRNAseq)-based deconvolution of genomic states of peripheral immune cells revealed distinct gene modules that were associated with COVID-19 outcome. Notably, monocytes exhibited bifurcated genomic states, with expression of a cytokine gene module exemplified by CCL4 (MIP-1{beta}) associated with survival and an interferon signaling module associated with death. These gene modules were correlated with higher levels of MIP-1{beta} and CXCL10 levels in plasma, respectively. Monocytes expressing genes reflective of these divergent modules were also detectable in endotracheal aspirates. Machine learning algorithms identified the distinctive monocyte modules as part of a multivariate peripheral immune system state that was predictive of COVID-19 mortality. Follow-up analysis of the monocyte modules on ICU day 5 was consistent with bifurcated states that correlated with distinct inflammatory cytokines. Our data suggests a pivotal role for monocytes and their specific inflammatory genomic states in contributing to mortality in life-threatening COVID-19 disease and may facilitate discovery of new diagnostics and therapeutics.

immunology↗

bioRxiv2021

Effect of Leaf Aqueous Extract of Manilkara hexandra on Digestive and Glucose Metabolic Enzymes, and Serum Glucose Level in Labeo rohita Fingerlings

Manilkara hexandra (Roxb.) Dubard (common name Khirni, family: Sapotaceae) is an evergreen traditionally used medicinal plant. The present study aimed to see the effects of leaf aqueous extract of M. hexandra (LAEMH) on digestive and glucose metabolic enzyme action and serum glucose level in rohu, Labeo rohita, fingerlings. Experimental fish were fed a basal diet (Group I), and diets supplemented with LAEMH at 300 mg (Group II) and 600 mg (Group III) kg-1body weight. A significant reduction in serum glucose was noticed in the treated groups when compared with the control group. The reduced amylase activity was noticed in the treated fingerlings in a dose-dependent manner. However, lipase and protease activities didnt differ significantly among the experimental groups. Reduced serum glucose level might be correlated with the decline in the activity of digestive amylase in fish. Group II (3.35{+/-}0.19 U) and Group III (3.49{+/-}0.13 U) were recorded with reduced glucose-6-phosphatase activities than the control group (4.4{+/-}0.39 U). Moreover, the study revealed a decline in fructose-1,6-phosphatase activities in the treated groups in comparison to the control group. The decline in the activities of the metabolic enzymes might be associated with the non-availability of glucose owing to reduced activity of the digestive amylase in the treated groups. In conclusion, the present study established the hypoglycaemic effect of the leaf aqueous extract of M. hexandra in a fish model.

pharmacology and toxicology↗

bioRxiv2020

Inferring Toll-Like Receptor induced epitope subunit vaccine candidate against SARS-CoV-2: A Reverse Vaccinology approach

Toll-Like Receptors (TLRs) are a group of Pattern Recognition Receptors (PRRs) which bind to the exogenous pathogen associated molecular patterns (PAMPs) like other PRRs; hence the main function is to sense the harmness and mediate the innate immune response to pathogens. TLRs play an important role in innate immune responses to infection. The host has evolved to use other TLR and PAMP agonists as agents to stimulate a protective inflammatory immune response against infection. Because only a small number of doses are given, TLR agonists appear to have greater potential and fewer safety concerns than other uses as vaccine adjuvants. In the present days, development of peptides targeting immune response can be approved for survival in biological monitoring systems before vaccine exposures. Peptide vaccines are easy to synthesize, more stable and relatively safe. In addition, production of peptides becomes simple, easily reproducible, fast and cost effective. Getting vaccinated against Covid-19, which has become a pandemic in the human population, is the most practical way to control the outbreak. The new coronavirus does not contain a drug or vaccine to prevent it from spreading to humans. To getting a proper vaccine candidate against the novel coronavirus, the present study used the reverse vaccinology approach by using a complete set of SARS-CoV-2 proteins; such as: Spike, Envelope, Nucleocapsid, Membrane, NSPs, and ORFs to extract the antigenic elements that produce B-cell, T-cell and IFN positive epitopes. These epitopes with precise binding to the Toll-Like receptors (1-10) have developed epitope based vaccine candidates. We have prioritized a set of epitopes based on their antigenicity, allergenicity, sequence conservation and projected population coverage world-wide. The selected epitopes were employed for in-silico docking interactions with Toll-Like receptors and molecular dynamic simulation confirmed the stability of the vaccine candidates resulting epitope of spike proteins with both the TLR 7 and 8 shows the best binding affinity. We believe that this ideal epitope vaccine candidate could enhance the immune response of the host and reduce the reinfection risk.

bioinformatics↗

bioRxiv2020

Leveraging High-Throughput Screening Data and Conditional Generative Adversarial Networks to Advance Predictive Toxicology

There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional Generative Adversarial Network (cGAN) and leveraging this large set of toxicity data, plus chemical structure information, we could efficiently predict toxic outcomes of untested chemicals. CAS numbers for each chemical were used to generate textual files containing three-dimensional structural information for each chemical. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first used regression (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train a generator to produce toxicity data. Our results showed that both Go-ZT and GAN-ZT models produce similar results, but the cGAN achieved a higher sensitivity (SE) value of 85.7% vs 71.4%. Conversely, Go-ZT attained higher specificity (SP), positive predictive value (PPV), and Kappa results of 67.3%, 23.4%, and 0.21 compared to 24.5%, 14.0%, and 0.03 for the cGAN, respectively. By combining both Go-ZT and GAN-ZT, our consensus model improved the SP, PPV, and Kappa, to 75.5%, 25.0%, and 0.211, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.663. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into untested areas of the chemical space to prioritize compounds for HT testing. SummaryA conditional Generative Adversarial Network (cGAN) can leverage a large chemical set of experimental toxicity data plus chemical structure information to predict the toxicity of untested compounds.

bioinformatics↗

bioRxiv2020

Turning traditional neurophysiology on its head: A naturalistic environment to study natural behaviors and cognitive tasks in freely moving monkeys

Macaque monkeys are widely used to study the neural basis of cognition. In the traditional approach, the monkey is brought into a lab to perform tasks while it is restrained to obtain stable gaze tracking and neural recordings. This unnatural setting prevents studying brain activity during natural, social and complex behaviors. Here, we designed a naturalistic environment with an integrated behavioral workstation that enables complex task training with viable gaze tracking in freely moving monkeys. We used this facility to train monkeys on a challenging same-different task. Remarkably, this facility enabled a naive monkey to learn the task merely by observing trained monkeys. This social training was faster primarily because the naive monkey first learned the task structure and then the same-different rule. We propose that such hybrid environments can be used to study brain activity during natural behaviors as well as during controlled cognitive tasks.

animal behavior and cognition↗

bioRxiv2020

High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next generation sequencing

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance and for determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for detection of SARS-CoV-2, with an additional advantage of enabling genetic epidemiology of SARS-CoV-2.

genomics↗

bioRxiv2020

Impact Analysis of SARS-CoV2 on Signaling Pathways during COVID19 Pathogenesis using Codon Usage Assisted Host-Viral Protein Interactions

Understanding the molecular mechanism of COVID19 disease pathogenesis helps in the rapid development of therapeutic targets. Usually, viral protein targets host proteins in an organized fashion. The pathogen may target cell signaling pathways to disrupt the pathway genes regular activities, resulting in disease. Understanding the interaction mechanism of viral and host proteins involved in different signaling pathways may help decipher the attacking mechanism on the signal transmission during diseases, followed by discovering appropriate therapeutic solutions. The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein-protein interactions (PPI). Exploiting the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the codon usage pattern similarity (and dissimilarity), we propose a computational scheme to recreate the hostviral protein interaction network (HVPPI). We use seventeen (17) essential signaling pathways for our current work and study the possible targeting mechanism of SARS-CoV2 viral proteins on such pathway proteins. We infer both negatively and positively interacting edges in the network. We can find a relationship where one host protein may target by more than one viral protein. Extensive analysis performed to understand the network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins, mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among non-structural proteins, NSP3 and structural protein, Spike (S) protein, are the most influential proteins in interacting multiple host proteins. In ranking the most affected pathways, MAPK pathways observe to be worst affected during the COVID-19 disease. A good number of targeted proteins are highly central in host protein interaction networks. Proteins participating in multiple pathways are also highly connected in their own PPI and mostly targeted by multiple viral proteins.

bioinformatics↗

bioRxiv2020

Comparative Analysis of Human Coronaviruses Focusing on Nucleotide Variability and Synonymous Codon Usage Pattern

Prevailing pandemic across the world due to SARSCoV-2 drawing great attention towards discovering its evolutionary origin. We perform an exploratory study to understand the variability of the whole coding region of possible proximal evolutionary neighbours of SARSCoV-2. We consider seven (07) human coronavirus strains from six different species as a candidate for our study. First, we observe a good variability of nucleotides across candidate strains. We did not find a significant variation of GC content across the strains for codon position first and second. However, we interestingly see huge variability of GC-content in codon position 3rd (GC3), and pairwise mean GC-content (SARSCoV, MERSCoV), and (SARSCoV-2, hCoV229E) are quite closer. While observing the relative abundance of dinucleotide feature, we find a shared typical genetic pattern, i.e., high usage of GC and CT nucleotide pair at the first two positions (P12) of codons and the last two positions (P23) of codons, respectively. We also observe a low abundance of CG pair that might help in their evolution bio-process. Secondly, Considering RSCU score, we find a substantial similarity for mild class coronaviruses, i.e., hCoVOC43, hCoVHKU1, and hCoVNL63 based on their codon hit with high RSCU value ([≥] 1.5), and minim number of codons hit (count-9) is observed for MERSCoV. We see seven codons ATT, ACT, TCT, CCT, GTT, GCT and GGT with high RSCU value, which are common in all seven strains. These codons are mostly from Aliphatic and Hydroxyl amino acid group. A phylogenetic tree built using RSCU feature reveals proximity among hCoVOC43 and hCoV229E (mild). Thirdly, we perform linear regression analysis among GC content in different codon position and ENC value. We observe a strong correlation (significant p-value) between GC2 and GC3 for SARSCoV-2, hCoV229E and hCoVNL63, and between GC1 and GC3 for hCoV229E, hCoVNL63, SARSCoV. We believe that our findings will help in understanding the mechanism of human coronavirus.

genomics↗

bioRxiv2020

Reagentless Biomolecular Analysis Using a Nanoscale Molecular Pendulum

The ability to sense biological inputs using self-contained devices unreliant on external reagents or reporters would open countless opportunities to collect information about our health and environment. Currently, a very limited set of molecular inputs can be detected using this type of sensor format. The development of versatile reagentless sensors that could track molecular analytes in biological fluids remains an unmet need. Here, we describe a new universal sensing mechanism that is compatible with the analysis of proteins that are important physiological markers of stress, allergy, cardiovascular health, inflammation and cancer. The sensing mechanism we developed is based on the measurement of field-induced directional diffusion of a nanoscale molecular pendulum tethered to an electrode surface and the sensitivity of electron-transfer reaction kinetics to molecular size. Using time-resolved electrochemical measurements of diffusional motion, the presence of an analyte bound to a sensor complex can be continuously tracked in real time. We show that this sensing approach is compatible with making measurements in blood, saliva, urine, tears and sweat and that the sensors can collect data in situ in living animals. The sensor platform described enables a broad range of applications in personalized health monitoring.

bioengineering↗

bioRxiv2019

Conversion of a defensive toxin-antitoxin system into an offensive T6SS effector in Burkholderia

Bacteria use various kinds of toxins to either inhibit the growth of co-habiting bacteria or when needed control their own growth. Here we report that Burkholderia and certain other bacteria have altered the potential defensive function of Tox-REase-5 domain containing toxins into offensive function. The Burkholderia gladioli strain NGJ1 encodes such toxins as type VI secretion system (T6SS) effectors (Tse) and potentially deploys them to kill co-habiting rice endophytic bacteria. Notably, the immunity (Tsi) proteins associated with Tse effectors demonstrate functional similarity with the antitoxin of type II toxin-antitoxin (TA) system. Genome analysis of diverse bacteria revealed that various Tse orthologs are either encoded as TA or T6SS effectors. In addition, potential evolutionary events associated with conversion of TA into T6SS effectors have been delineated. Our results indicate that the transposition of IS3 elements has led to the operonic fusion of certain T6SS related genes with TA genes resulting in their conversion into T6SS effectors. Such a genetic change has enabled bacteria to utilize novel toxins to precisely target co-habiting bacteria.

microbiology↗

bioRxiv2019

Thermodynamic impacts of combinatorial mutagenesis on protein conformational stability: precise, high-throughput measurement by Thermofluor

The D1 switch is a packing motif, broadly distributed in the proteome, that couples tryptophanyl-tRNA synthetase (TrpRS) domain movement to catalysis and specificity, thereby creating an escapement mechanism essential to free-energy transduction. The escapement mechanism arose from analysis of an extensive set of combinatorial mutations to this motif, which allowed us to relate mutant-induced changes quantitatively to both kinetic and computational parameters during catalysis. To further characterize the origins of this escapement mechanism in differential TrpRS conformational stabilities, we use high-throughput Thermofluor measurements for the 16 variants to extend analysis of the mutated residues to their impact on unliganded TrpRS stability. Aggregation of denatured proteins complicates thermodynamic interpretations of denaturation experiments. The free energy landscape of a liganded TrpRS complex, carried out for different purposes, closely matches the volume, helix content, and transition temperatures of Thermoflour and CD melting profiles. Regression analysis using the combinatorial design matrix accounts for >90% of the variance in Tms of both Thermofluor and CD melting profiles. We argue that the agreement of experimental melting temperatures with both computational free energy landscape and with Regression modeling means that experimental melting profiles can be used to analyze the thermodynamic impact of combinatorial mutations. Tertiary packing and aromatic stacking of Phenylalanine 37 exerts a dominant stabilizing effect on both native and molten globular states. The TrpRS Urzyme structure remains essentially intact at the highest temperatures explored by the simulations.

biophysics↗

bioRxiv2018

Cryo-Electron tomography 3D structure and nanoscale model of Arabidopsis thaliana cell wall

Using cryo-electron tomography of vitrified sections of one month-old Arabidopsis thaliana inflorescence stem tissue, we visualized primary and secondary cell walls of xylem tissue. Extensive quantitative and statistical analysis of segmented 3D tomographic data allowed geometrically idealized 3D-CAD model building of prototypic microfibrils, cross-links, and their supramolecular microfibril 3D organization. We propose a prototypic microfibril model where a cellulose core is heavily decorated by a thin sheath of hemicellulose with infrequent but sturdy hemicellulose-based cross-links. Such prototypic microfibrils then adopt a rather unexpected 3D supramolecular organization of high order and complexity. We discuss a possible new role for lignin in plant cell walls at low concentrations with lignin not acting as a matrix but rather as a reinforcement of microfibrils and cross-links. Extensive computational simulations of mechanical properties further revealed that this 3D organization of the cell wall is not optimized for load bearing but instead for flexibility and ductility.\n\nOne Sentence SummaryCryo-electron tomography and mechanical simulations revealed cell wall 3D architecture, optimized for flexibility/ductility.

plant biology↗

bioRxiv2018

Mutations in bacterial genes induce unanticipated changes in the relationship between bacterial pathogens in experimental otitis media

Otitis media (OM) is a common polymicrobial infection of the middle ear in children under the age of fifteen years. A widely used experimental strategy to analyze roles of specific phenotypes of bacterial pathogens of OM is to study changes in co-infection kinetics of bacterial populations in animal models when a wild type bacterial strain is replaced by a specific isogenic mutant strain in the co-inoculating mixtures. Since relationships between the OM bacterial pathogens within the host are regulated by many interlinked processes, connecting the changes in the co-infection kinetics to a bacterial phenotype can be challenging. We investigated middle ear co-infections in adult chinchillas (Chinchilla lanigera) by two major OM pathogens: nontypeable Haemophilus influenzae (NTHi) and Moraxella catarrhalis (Mcat), as well as isogenic mutant strains in each bacterial species. We analyzed the infection kinetic data using Lotka-Volterra population dynamics, Maximum Entropy inference, and Akaike Information Criteria (AIC) based model selection. We found that changes in relationships between the bacterial pathogens that were not anticipated in the design of the co-infection experiments involving mutant strains are common and were strong regulators of the co-infecting bacterial populations. The framework developed here allows for a systematic analysis of host-host variations of bacterial populations and small sizes of animal cohorts in co-infection experiments to quantify the role of specific mutant strains in changing the infection kinetics. Our combined approach can be used to analyze the functional footprint of mutant strains in regulating co-infection kinetics in models of experimental OM and other polymicrobial diseases.

microbiology↗

bioRxiv2018

Mathematical Modeling Identifies the Role of Adaptive Immunity as a Key Controller of Respiratory Syncytial Virus (RSV) Titer in Cotton Rats

Respiratory syncytial virus (RSV) is a common virus that can have varying effects ranging from mild cold-like symptoms to mortality depending on the age and immune status of the individual. We combined mathematical modeling using ordinary differential equations (ODEs) with measurement of RSV infection kinetics in primary well differentiated human airway epithelial (HAE) cultures in vitro and in immunocompetent and immunosuppressed cotton rats to glean mechanistic details that underlie RSV infection kinetics in the lung. Quantitative analysis of viral titer kinetics in our mathematical model showed that the elimination of infected cells by the adaptive immune response generates unique RSV titer kinetic features including a faster time scale of viral titer clearance than viral production, and a monotonic decrease in the peak RSV titer with decreasing inoculum dose. Parameter estimation in the ODE model using a non-linear mixed effects approach revealed a very low rate (average single cell lifetime > 10 days) of cell lysis by RSV before the adaptive immune response is initiated. Our model predicted negligible changes in the RSV titer kinetics on earlier days (< 5 d.p.i) but a slower decay in RSV titer at later days (>5 d.p.i) in immunosuppressed cotton rats compared to that in non-suppressed cotton rats in silico. These predictions were in excellent agreement with the experimental results. Our combined approach quantified the importance of the adaptive immune response in suppressing RSV infection in cotton rats, which could be useful in testing RSV vaccine candidates.\n\nImportanceA major difficulty in developing vaccines against RSV infection is our rudimentary understanding of the mechanisms that underlie RSV infection. We addressed this challenge by developing a mechanistic computational model with predictive powers for describing RSV infection kinetics in cotton rats. The model was constructed synergistically with in vitro and in vivo measurements. The combined framework determined an important role for CD8+ T cells responses in reducing RSV titers in cotton rats. The framework can be used to design future experiments to elucidate mechanisms underlying RSV infection and test outcomes for potential vaccine candidates. In addition, estimation of the model parameters provides quantitative values for parameters of biological and clinical interest such as the replication rate of RSV, the death rate of infected cells, and the average number of new infections initiated by a single infected cell.

microbiology↗

bioRxiv2018

Mathematical Modeling Identifies the Role of Adaptive Immunity as a Key Controller of Respiratory Syncytial Virus (RSV) Titer in Cotton Rats

Respiratory syncytial virus (RSV) is a common virus that can have varying effects ranging from mild cold-like symptoms to mortality depending on the age and immune status of the individual. We combined mathematical modeling using ordinary differential equations (ODEs) with measurement of RSV infection kinetics in primary well differentiated human airway epithelial (HAE) cultures in vitro and in immunocompetent and immunosuppressed cotton rats to glean mechanistic details that underlie RSV infection kinetics in the lung. Quantitative analysis of viral titer kinetics in our mathematical model showed that the elimination of infected cells by the adaptive immune response generates unique RSV titer kinetic features including a faster time scale of viral titer clearance than viral production, and a monotonic decrease in the peak RSV titer with decreasing inoculum dose. Parameter estimation in the ODE model using a non-linear mixed effects approach revealed a very low rate (average single cell lifetime > 10 days) of cell lysis by RSV before the adaptive immune response is initiated. Our model predicted negligible changes in the RSV titer kinetics on earlier days (< 5 d.p.i) but a slower decay in RSV titer in immunosuppressed cotton rats compared to that in non-suppressed cotton rats at later days (>5 d.p.i) in silico. These predictions were in excellent agreement with the experimental results. Our combined approach quantified the importance of the adaptive immune response in suppressing RSV infection in cotton rats, which could be useful in testing RSV vaccine candidates.\n\nImportanceA major difficulty in developing vaccines against RSV infection is our rudimentary understanding of the mechanisms that underlie RSV infection. We addressed this challenge by developing a mechanistic computational model with predictive powers for describing RSV infection kinetics in cotton rats. The model was constructed synergistically with in vitro and in vivo measurements. The combined framework determined an important role for CD8+ T cells responses in reducing RSV titers in cotton rats. The framework can be used to design future experiments to elucidate mechanisms underlying RSV infection and test outcomes for potential vaccine candidates. In addition, estimation of the model parameters provides quantitative values for parameters of biological and clinical interest such as the replication rate of RSV, the death rate of infected cells, and the average number of new infections initiated by a single infected cell.

microbiology↗