Biology subjects

Saxena, S.

Publications and source records attributed to Saxena, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Unlocking the Power of CXCR2 Inhibition to Overcome Gemcitabine Resistance in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related mortality, characterized by intrinsic resistance to conventional therapies and limited effective treatment options. In this study, we investigated the role of the CXCR2 axis in PDAC therapy resistance. CXCR2, a chemokine receptor, is actively involved in inflammation, tumor angiogenesis, and metastasis. Our working hypothesis is that CXCR2 contributes to PDAC chemotherapy resistance. To test this, we generated gemcitabine-resistant (GemR) lines using T3M4 and CD18/HPAF (CD18) cell lines. Baseline expression of CXCL1, CXCL5, and CXCL8 ligands were higher in GemR cells compared to parental cells. Upon gemcitabine treatment, parental cells exhibited a greater increase in CXCL1 and CXCL8 expression than GemR cells. Further analysis in T3M4 cells revealed a dose- and time-dependent increase in CXCL1 and CXCL8 expression following gemcitabine exposure. Next, we assessed whether targeting CXCR2 could enhance the therapeutic response. We treated parental and GemR cell lines with gemcitabine in combination with the CXCR2 antagonist SCH 479833 (SCH 47). Notably, lower concentrations of gemcitabine combined with SCH 47 were more effective than higher concentrations of gemcitabine alone in GemR cell lines. In both parental and GemR xenograft models, combination therapy with Navarixin (a CXCR2 antagonist) and gemcitabine demonstrated superior antitumor and antimetastatic activity compared to either treatment alone. In conclusion, these findings highlight the critical role of the CXCR2 axis in PDAC therapy resistance. Targeting CXCR2 enhances gemcitabine efficacy, offering a potential therapeutic strategy to overcome resistance in PDAC.

cancer biology↗

bioRxiv2025

Genome-Wide CRISPR Screening Identifies Cellular Factors Controlling Nonviral Genome Editing Efficiency

After administering genome editors, their efficiency is limited by a multi-step process involving cellular uptake, trafficking, and nuclear import of the vector and its payload. These processes vary widely across cell types and differ depending on the nature and structure of the vector, whether it is a lipid nanoparticle or a different synthetic material. We developed a novel genome-wide CRISPR screening strategy to better understand these limitations within human cells to identify genes modulating cellular uptake, payload delivery, and gene editing efficiency. Our screen interrogates the cellular processes controlling genome editing by Cas-based nuclease and base editing strategies in human cells. We designed a genome-wide screen targeting 19,114 genes in HEK293 cells, and we identified six genes whose knockout increased nonviral editing efficiency in human cells by up to five-fold. Further validation through arrayed knockouts of the top hits from our screen boosted the editing efficiency from 5% to 50% when Cas9 was delivered via lipid-based nanoparticles. By designing the guides to target the screen library cassette, we could accurately track the library sgRNA identity and the editing outcome on the same amplicon via short-read sequencing, enabling the identification of rare outcomes via computationally sorting edited from unedited cells within a heterogenous pool of >200M cells. In patient-derived human retinal pigment epithelium cells derived from pluripotent stem cells, BET1L, GJB2, and MS4A13 gene knockouts increased targeted genome editing by over five-fold. We anticipate that this high-throughput screening approach will facilitate the systematic engineering of novel nonviral genome editing delivery methods, where the identified novel gene hits can be further used to increase editing efficiency for other therapeutically relevant cell types.

genomics↗

bioRxiv2024

The stage-specific regulation imposed by Importin beta-1 on HIV-1 propagation and infectivity dynamics

Dynamics of intra-host molecular evolution of viruses depend on the complexities of the cellular environments through which they transit. HIV infects several cell types in an infected human host, especially during chronic stages, which impose differential regulations on HIV persistence, driving it to latency, rapid propagation, or abortive infection. We observed that HIV-1 emerging from different cell-types differ in their encapsidated protein cargo, and infectivity. We investigated the role of Importin{beta}-1, which is encapsidated in virus emerging from CD4+T lymphocytes, but not in viruses from astrocytes. We deciphered that Importin{beta}-1 is packaged via interactions with HIV-1 Gag/Capsid. The encapsidated Importin{beta}-1 assisted nuclear-entry of the viral core and enhanced the infectivity during pre-integration stages. Conversely, high levels of endogenous Importin{beta}-1, which was observed to be induced upon infection and inflammatory stimulations, such as, IFNg/LPS treatments, restricted LTR-driven viral transcription during post-integration. The regulatory impact of Importin{beta}-1 was verified using primary CD4+T lymphocytes, thereby validating a non-canonical and novel role of Importin{beta}-1 as a restriction factor for HIV-1. Using deletion mutants, we demonstrate that the N-terminal domain of Importin{beta}-1 regulated viral transcription via SP1, and NRE regions of LTR. We propose that sequestering of Importin{beta}-1 by packaging in emerging virions thereby reducing its antiviral impact, is an adaptive strategy of a pathogen, supporting the concepts of evolutionary conflicts between viruses and hosts.

molecular biology↗

bioRxiv2024

Unprocessed Genomic Uracil as a Source of DNA Replication Stress in Cancer Cells

Alterations of bases in DNA constitute a major source of genomic instability. It is believed that base alterations trigger base excision repair (BER), generating DNA repair intermediates interfering with DNA replication. Here, we show that genomic uracil, a common base alteration, induces DNA replication stress (RS) without being processed by BER. In the absence of uracil DNA glycosylase (UNG), genomic uracil accumulates to high levels, DNA replication forks slow down, and PrimPol-mediated repriming is enhanced, generating single-stranded gaps in nascent DNA. ATR inhibition in UNG-deficient cells blocks repair of uracil-induced gaps, increasing replication fork collapse and cell death. Notably, a subset of cancer cells harboring high levels of genomic uracil upregulate UNG2 to limit RS, and these cancer cells are hypersensitive to co-treatment with ATR inhibitors and drugs increasing genomic uracil. These results reveal unprocessed genomic uracil as an unexpected source of RS and a targetable vulnerability of cancer cells.

molecular biology↗

bioRxiv2024

μSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling

How does the motor cortex (MC) produce purposeful and generalizable movements with the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate a model of MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory feedback from biologically accurate musculoskeletal models. Given this real-time simulated feedback implemented in advanced physics simulation engines, we use deep reinforcement learning to train the RNN to execute desired movements under specified neural and musculoskeletal constraints. For general use, we provide a modular computational framework that allows the flexible integration of user-defined musculoskeletal models, training algorithms, tasks and constraints. We also provide a combination of modules to analyze and quantify the dynamical alignment and similarity of the trained RNN with the recorded neural data on the population and single-unit level. Using these modules, we find that the activity of the trained RNN can accurately decode experimentally recorded neural population dynamics and single-unit MC activity, while generalizing well to testing conditions significantly different from training. Finally, we also provide perturbation modules to generate insights about neural dynamics for perturbed conditions different from training, and show that this framework unveils computational principles of how such neural dynamics enable flexible control of movement.

neuroscience↗

bioRxiv2023

Endoplasmic reticulum exit sites are segregated for secretion based on cargo size

TANGO1-family proteins (TANGO1, TANGO1S and cTAGE5) form stable complexes at the Endoplasmic Reticulum Exit Sites (ERES) and mediate export of bulky cargoes. The C-terminal proline rich domain (PRD) of these proteins binds Sec23A and affects COPII assembly at ERES. These PRD interactions were replaced with light-responsive domains to control the binding between TANGO1S-DPRD and Sec23A. TANGO1S{Delta}PRD was dispersed in the ER membrane but relocated rapidly, yet reversibly, to pre-exiting ERES by binding to Sec23A upon light-activation. Prolonged binding of these two proteins concentrated ERES in the juxtanuclear region by a microtubule dependent process, blocked secretory cargo export and relocated ERGIC53 into the ER, but had limited impact on Golgi complex organization. Under these conditions, bulky collagen VII, and endogenous collagen I were collected at less than 47% of the stalled ERES, whereas small cargo molecules were halted uniformly across the ER, indicating that ERES differentially adapt to cargo size. We suggest these differences in cargo-accumulation at ERES permit cells to balance trafficking of cargoes of different sizes and optimize secretion.

cell biology↗

bioRxiv2023

Behavioral Classification of Sequential Neural Activity Using Time Varying Recurrent Neural Networks

Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in devising corrective neural stimulation before the onset of behavior. Recurrent Neural Networks (RNNs) are common models for sequence data. However, standard RNNs are not able to handle data with temporal distributional shifts to guarantee robust classification across time. To enable the network to utilize all temporal features of the neural input data, and to enhance the memory of an RNN, we propose a novel approach: RNNs with time-varying weights, here termed Time-Varying RNNs (TV-RNNs). These models are able to not only predict the class of the time-sequence correctly but also lead to accurate classification earlier in the sequence than standard RNNs. In this work, we focus on early sequential classification of brain-wide neural activity across time using TV-RNNs applied to a variety of neural data from mice and humans, as subjects perform motor tasks. Finally, we explore the contribution of different brain regions on behavior classification using SHapley Additive exPlanation (SHAP) value, and find that the somatosensory and premotor regions play a large role in behavioral classification.

neuroscience↗

bioRxiv2022

Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)

Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior in naturalistic settings in social and multi-subject tasks remains a significant challenge. Modeling the behavior of different subjects performing the same task requires partitioning the behavioral data into features that are common across subjects, and others that are distinct to each subject. Modeling social interactions between multiple individuals in a freely-moving setting requires disentangling effects due to the individual as compared to social investigations. To achieve flexible disentanglement of behavior into interpretable latent variables with individual and across-subject or social components, we build on a semi-supervised approach to partition the behavioral subspace, and propose a novel regularization based on the Cauchy-Schwarz divergence to the model. Our model, known as the constrained subspace variational autoencoder (CS-VAE), successfully models distinct features of the behavioral videos across subjects, as well as continuously varying differences in social behavior. Our approach vastly facilitates the analysis of the resulting latent variables in downstream tasks such as uncovering disentangled behavioral motifs, the efficient decoding of a novel subjects behavior, and provides an understanding of how similarly different animals perform innate behaviors.

neuroscience↗

bioRxiv2022

Biochemical analysis of anthocyanin and proanthocyanidin and their regulation in determining chickpea flower and seed coat colours

Flower and seed coat colour are important agronomic traits in chickpea (Cicer arietinum L.). Cultivated chickpeas are of two types namely, desi (dark seeded, purple flower) and kabuli (light colour seeded, white flower). There has been limited information about the molecular mechanism underlying the colour variation of flower and seed coats in desi and kabuli chickpea. We profiled the anthocyanin and proanthocyanidin (PA) contents in chickpea flowers and seed coats. Tissue-specific silencing of two genes encoding a basic helix-loop-helix (CabHLH) protein and a tonoplast-localized multidrug and toxic compound extrusion (CaMATE1) transporter in a desi genotype resulted in the reduction in expressions of anthocyanin and PA biosynthetic genes and anthocyanin and PA contents in the flower and seed coat and produced flowers and seeds with kabuli characteristics. Transcriptional regulation of a subset of anthocyanin and PA biosynthetic genes by a natural CabHLH variant and transport assay of a natural CaMATE1 variant explained the association of these alleles with the kabuli phenotype. We carried out a detailed molecular characterization of these genes, and provided evidences that kabuli chickpea flower and seed colour phenotype can be derived by manipulation of single genes in a desi chickpea background. HighlightIn this study, we have defined the molecular link between flower and seed color in chickpea and identified CaMATE1 and CabHLH as the regulators of both the traits.

plant biology↗

bioRxiv2022

Changes in m6A RNA methylation of goat lung following PPRV infection

Peste des petits ruminants (PPR) is an acute, highly contagious viral disease of goats and sheep, caused by the Peste des petits ruminants virus (PPRV). Earlier studies suggest the involvement of diverse regulatory mechanisms in PPRV infection. Methylation at N6 of Adenosine called m6A is one such RNA modification that influences various physiological and pathological phenomena. As the lung tissue represents the primary target organ of PPRV, the present study explored the m6A changes and their functional significance in PPRV disease pathogenesis. m6A-seq analysis revealed 1289 m6A peaks to be significantly altered in PPRV infected lung in comparison to normal lung, out of which 975 m6A peaks were hypomethylated and 314 peaks were hypermethylated. Importantly, hypomethylated genes were enriched in Interleukin-4 and Interleukin-13 signaling and various processes associated with extracellular matrix organization. Further, of the 843 differentially m6A containing cellular transcripts, 282 transcripts were also found to be differentially expressed. Functional analysis revealed that these 282 transcripts are significantly enriched in signaling by Interleukins, extracellular matrix organization, cytokine signaling in the immune system, signaling by receptor tyrosine kinases, and Toll-like Receptor Cascades. We also found m6A reader HNRNPC and the core component of methyltransferase complex METTL14 to be highly upregulated than the m6A readers - HNRNPA2B1 and YTHDF1 at the transcriptome level. These findings suggest that alteration in m6A landscape following PPRV is implicated in diverse processes including the Interleukin signaling.

microbiology↗

bioRxiv2022

Motor cortical influence relies on task-specific activity covariation

During limb movement, spinal circuits facilitate the alternating activation of antagonistic flexor and extensor muscles. Yet antagonist cocontraction is often required to stabilize joints, like when loads are handled. Previous results suggest that these different muscle activation patterns are mediated by separate flexion- and extension-related motor cortical output populations, while others suggest recruitment of task-specific populations. To distinguish between hypotheses, we developed a paradigm in which mice toggle between forelimb tasks requiring antagonist alternation or cocontraction and measured activity in motor cortical layer 5b. Our results conformed to neither hypothesis: consistent flexion- and extension-related activity was not observed across tasks, and no task-specific populations were observed. Instead, activity covariation among motor cortical neurons dramatically changed between tasks, thereby altering the relation between neural and muscle activity. This was also observed specifically for corticospinal neurons. Collectively, our findings indicate that motor cortex drives different muscle activation patterns via task-specific activity covariation. HIGHLIGHTSO_LIMice perform two forelimb tasks involving distinct antagonist muscle activity in a novel paradigm C_LIO_LIL5b motor cortical neurons are not organized by task-specific activity C_LIO_LIL5b motor cortical neurons do not encode muscle activity consistently across tasks C_LIO_LITask-specific muscle activity is driven by a change in motor cortical activity covariation C_LI eTOC BLURBWarriner et al. simultaneously measured muscle and motor cortical activity in mouse during antagonist forelimb muscle alternation and cocontraction, revealing that these distinct muscle activation patterns are not driven through consistent flexion and extension programs nor through the activity of discrete, task-specific neuronal subsets. Instead, distinct patterns involve task-specific changes in firing pattern covariation among layer 5b neurons, and corticospinal neurons in particular, which change their relationship to muscle activity across tasks.

neuroscience↗

bioRxiv2021

Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement

Volition - the sense of control or agency over ones voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. Several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. Others propose that random processes underlie and explain pre-movement neural activity. Here we seek to address these issues by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls could be predicted between 3 to 5 seconds (or more in some cases) prior to movement. We found inhibition of motor cortex starting at approximately - 5sec prior to lever pulls and activation of motor cortex starting at approximately 2sec prior to a random unrewarded left limb movement. We show that mice, like humans, are biased towards commencing self-initiated actions during specific phases of neural activity but that the pre-movement neural code changes over time in some mice and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding self-initiated action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated action could be preserved between mice and humans.

neuroscience↗

bioRxiv2021

nucGEMs probe the biophysical properties of the nucleoplasm

The cell interior is highly crowded and far from thermodynamic equilibrium. This environment can dramatically impact molecular motion and assembly, and therefore influence subcellular organization and biochemical reaction rates. These effects depend strongly on length-scale, with the least information available at the important mesoscale (10-100 nanometers), which corresponds to the size of crucial regulatory molecules such as RNA polymerase II. It has been challenging to study the mesoscale physical properties of the nucleoplasm because previous methods were labor-intensive and perturbative. Here, we report nuclear Genetically Encoded Multimeric nanoparticles (nucGEMs). Introduction of a single gene leads to continuous production and assembly of protein-based bright fluorescent nanoparticles of 40 nm diameter. We implemented nucGEMs in budding and fission yeast and in mammalian cell lines. We found differences in particle motility between the nucleus and the cytosol at the mesoscale, that mitotic chromosome condensation ejects nucGEMs from the nucleus, and that nucGEMs are excluded from heterochromatin and the nucleolus. nucGEMs enable hundreds of nuclear rheology experiments per hour, and allow evolutionary comparison of the physical properties of the cytosol and nucleoplasm.

biophysics↗

bioRxiv2021

Identification of SGLT2 inhibitor Ertugliflozin as a treatment for COVID-19 using computational and experimental paradigm

Drug repurposing can expedite the process of drug development by identifying known drugs which are effective against SARS-CoV-2. The RBD domain of SARS-CoV-2 Spike protein is a promising drug target due to its pivotal role in viral-host attachment. These specific structural domains can be targeted with small molecules or drug to disrupt the viral attachment to the host proteins. In this study, FDA approved Drugbank database were screened using a virtual screening approach and computational chemistry methods. Five drugs were short listed for further profiling based on docking score and binding energies. Further these selected drugs were tested for their in vitro biological activity. There was significant correlation between the prediction from computational studies and the actual RBD-ACE2 binding inhibition by the drugs. Then, we performed a series of studies that mimic some of the biological events seen in COVID-19 patients such as secretion of IL1{beta}, presentation of a more thrombogenic endothelium by production of thrombomodulin and accumulation of inflammatory cells such as monocytes in the lungs. Of all the drugs, most promising drug was Ertugliflozin which is used for type-2 diabetes. This drug possesses several desired properties and may be a good candidate for immediate repurposing for treatment of COVID-19.

pharmacology and toxicology↗

bioRxiv2021

Tyrosine Kinase Inhibitor Family of Drugs as Prospective Targeted Therapy for COVID-19 Based on In Silico And 3D-Human Vascular Lung Model Studies

COVID-19 pandemic has ravaged the world and vaccines have been rapidly developed as preventive measures. But there is no target-based therapy which can be used if infection sets in. Remdesiver and dexamethasone were not designed to combat COVID-19 but are used clinically till better targeted therapies are available. Given this situation target based therapies that intervene in the disease pathway are urgently needed. Since COVID-19 genesis is driven by uncontrolled inflammation/thrombosis and protein kinases are critical in mounting this response, we explored if available tyrosine kinase inhibitors (TKIs) can be used as intervention. We profiled four TKIs namely; Lapatinib, Dasatinib, Pazopanib and Sitravatinib which inhibit tyrosine kinases but are completely distinct in their chemical structures. We demonstrate using in silico and an in vitro 3D-human vascular lung model which profiles anti-inflammatory and anti-thrombogenic properties that all four TKIs are active in varying degrees. Our findings that chemically different TKIs which share kinase inhibition as the common mechanism of action are active, strongly indicates that its a tyrosine kinase target-based activity and not off-target arbitrary effect. We propose that TKIs, approved for human use and widely available, can be rapidly deployed as specific target-based therapy for COVID-19.

pharmacology and toxicology↗

bioRxiv2021

In silico and in vitro Demonstration of Homoharrintonine Antagonism of RBD-ACE2 Binding and its Anti-inflammatory and anti-thrombogenic Properties in a 3D human vascular lung model

Since 2019 the world has seen severe onslaught of SARS-CoV-2 viral pandemic. There is an urgent need for drugs that can be used to either prevent or treat the potentially fatal disease COVD-19. To this end, we screened FDA approved antiviral drugs which could be repurposed for COVID-19 through molecular docking approach in the various active sites of receptor binding domain (RBD). The RBD domain of SARS-CoV-2 spike protein is a promising drug target due to its pivotal role in viral-host attachment. Specifically, we focussed on identifying antiviral drugs which could a) block the entry of virus into host cells, b) demonstrate anti-inflammatory and/or anti-thrombogenic properties. Drugs which poses both properties could be useful for prevention and treatment of the disease. While we prioritized a few antiviral drugs based on molecular docking, corroboration with in vitro studies including a new 3D human vascular lung model strongly supported the potential of Homoharringtonine, a drug approved for chronic myeloid leukaemia to be repurposed for COVID-19. This natural product drug not only antagonized the biding of SARS-CoV-2 spike protein RBD binding to human angiotensin receptor 2 (ACE-2) protein but also demonstrated for the first time anti-thrombogenic and anti-leukocyte adhesive properties in a human cell model system. Overall, this work provides an important lead for development of rapid treatment of COVID-19 and also establishes a screening paradigm using molecular modelling and 3D human vascular lung model of disease to identify drugs with multiple desirable properties for prevention and treatment of COVID-19.

pharmacology and toxicology↗

bioRxiv2021

Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

neuroscience↗

bioRxiv2020

Effect of earthworms in removal and fate of Antibiotic resistant bacteria (ARB) and antibiotic resistant genes (ARG) during clinical laboratory wastewater treatment by vermifiltration

The wastewater treatment plants effluent has been implicated in the spread of antibiotic resistant bacteria (ARB) as these environment contains multiple selective pressures that may increase mutation rates, pathogen survivability, and induce gene transfer between bacteria. In lieu of this, the present study explored the dynamics of earthworm-microorganisms interactions on the treatment efficacy of clinical laboratory wastewater treatment by vermifiltration and the effect of earthworms in the fate of removal of pathogens and ARB. The results of the study showed that earthworms and VF associated microbial community had a significant effect on BOD and COD reduction (78-85%), pathogen removal (>99.9 %) and caused a significant shift in the prevalence pattern of ARB. Additionally, molecular profiling of ESBL (blaSHV, blaTEM and blaCTX-M), MRSA (mec-A) and Colistin (mcr-1) gene confirmed the probable mechanisms behind the resistance pattern. The microbial community diversity assists in the formation of biofilm, which helps in the removal of pathogens and results in a paradigm shift in the resistance profile of ARB and ARG, specifically most effective against drugs, targeting cell wall and protein synthesis inhibition like Ampicillin, Ticarcillin, Gentamicin and Chloramphenicol. These findings prove vermifiltration technology as a sustainable and natural treatment technology for clinical laboratory wastewater.

ecology↗