Biology subjects

Kaur, D.

Publications and source records attributed to Kaur, D..

At least 19 recordsLinked to original sources

bioRxiv2024

Characterization of bacteria colonizing the mucosal layer of the gastrointestinal tract of Atlantic salmon farmed in a warm water region

Atlantic salmon (Salmo salar) farmed in seawater in Tasmania (lutruwita) can experience temperatures close to their thermotolerance limit during summer. Gut microbiome data from eight successive annual surveys and a specific survey of GI tract mucosa and digesta bacterial cross-sectional distributions indicated members of the genus Aliivibrio, Vibrio, and an unclassified Mycoplasmoidaceae are the main colonizers of the gut mucosal layer in Tasmanian farmed salmon. Peak abundance levels are reached after 7-8 months, corresponding to the late summer, following the transfer of hatchery smolt to seawater farms. Salmon Aliivibrio isolates comprise three novel non-bioluminescent species distinct from species found in the northern hemisphere growing regions. Along with other Aliivibrio species these species share many genes required for host colonization and biofilm formation and also include species and strain-level dependent features. Two of the novel Aliivibrio species surprisingly possessed genes for cytolethal distending toxin while the more predominant species lacked any known virulence genes. The overall observations suggest a small number of species actively colonize the mucosal layer of Atlantic salmon farmed in Tasmania and that this process is strongly influenced by the environmental temperature.

microbiology↗

bioRxiv2024

Identification of Juglone, a "first-in-class" inhibitor of the human glutathione degrading enzyme, ChaC1, using yeast-based high throughput screens

The cytosolic glutathione-degrading enzyme, ChaC1, is highly upregulated in several cancers, with the upregulation correlating to poor prognosis. The ability to inhibit ChaC1 thus becomes important in pathophysiological situations where elevated glutathione levels would be beneficial. As no inhibitors of ChaC1 are known, in this study we have focussed on this goal. We have initially taken a computational approach where a systemic structure-based virtual screening was performed. However, none of the predicted hits proved to be effective inhibitors. We also evaluated synthetic substrate analogs, but these too were not inhibitory. As both these approaches targeted the active site, we shifted to developing two high-throughput, robust, yeast-based assays that were active site independent. A small molecule compound library was screened using an automated liquid handling system using these screens. The hits were further analyzed using in vitro assays. Among them, juglone, a naturally occurring naphthoquinone, completely inhibited ChaC1 activity with an IC50 of 8.7 {micro}M. It was also effective against the ChaC2 enzyme. Kinetic studies indicated that the inhibition was not competitive with the substrate. Juglone is known to form adducts with glutathione and is also known to selectively inhibit enzymes by covalently binding to their active site cysteine residues. However, juglone continued to inhibit a cysteine-free ChaC1 variant, indicating that it was acting through a novel mechanism. We evaluated different inhibitory mechanisms, and also analogues of juglone, and found plumbagin effective as an inhibitor. These compounds represent the first-in-class inhibitors of the ChaC enzymes discovered using a robust yeast screen.

biochemistry↗

bioRxiv2023

Estrogenic control of reward prediction errors and reinforcement learning

Gonadal hormones act throughout the brain1, and neuropsychiatric disorders vary in symptom severity over the reproductive cycle, pregnancy, and perimenopause2-4. Yet how hormones influence cognitive processes is unclear. Exogenous 17{beta}-estradiol modulates dopamine signaling in the nucleus accumbens core (NAcc)5,6, which instantiates reward prediction errors (RPEs) for reinforcement learning7-16. Here we show that endogenous 17{beta}-estradiol enhances RPEs and sensitivity to previous rewards by reducing dopamine reuptake proteins in the NAcc. Rats performed a task with different reward states; they adjusted how quickly they initiated trials across states, balancing effort against expected rewards. NAcc dopamine reflected RPEs that predicted and causally influenced initiation times. Elevated endogenous 17{beta}-estradiol increased sensitivity to reward states by enhancing dopaminergic RPEs in the NAcc. Proteomics revealed reduced dopamine transporter expression. Finally, knockdown of midbrain estrogen receptors suppressed reinforcement learning. 17{beta}-estradiol therefore controls RPEs via dopamine reuptake, mechanistically revealing how hormones influence neural dynamics for motivation and learning.

neuroscience↗

bioRxiv2023

Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade the respiratory microbiome

The emergence and spread of antibiotic resistance in bacterial pathogens is a global health threat. One important unanswered question is how antibiotic resistance influences the ability of a pathogen to invade the host-associated microbiome. Here we investigate how antibiotic resistance impacts the ability of the opportunistic bacterial pathogen Pseudomonas aeruginosa to invade the respiratory microbiome, by measuring the ability of P. aeruginosa spontaneous antibiotic resistant mutants to invade pre-established cultures of commensal respiratory microbes. We find that commensal respiratory microbes tend to inhibit the growth of P. aeruginosa, and antibiotic resistance is a double-edged sword that can either help or hinder the ability of P. aeruginosa to overcome this inhibition. The directionality of this help or hinderance depends on both P. aeruginosa genotype and respiratory microbe identity. Antibiotic resistance facilitates the invasion of P. aeruginosa into Staphylococcus lugdunensis, yet impairs invasion into Rothia mucilaginosa and Staphylococcus epidermidis. Streptococcus species provide the strongest inhibition to P. aeruginosa invasion, and this is maintained regardless of antibiotic resistance genotype. Our study demonstrates how antibiotic resistance can alter the ability of a bacterial pathogen to invade the respiratory microbiome and suggests that attempts to manipulate the microbiome should focus on promoting the growth of commensals that can provide robust inhibition of both wildtype and antibiotic resistant pathogen strains.

microbiology↗

bioRxiv2023

Untangling the Role of Pathobionts from Bacteroides Species in Inflammatory Bowel Diseases

Inflammatory bowel diseases (IBD) arise from a convergence of underlying genetic susceptibility, environmental factors, and shifts in gut microbiota function and membership. Although the latter may trigger and contribute to IBD, there is little consensus on a specific causative pathogen. In this study, we demonstrate that commensal Bacteroides fragilis strains from ulcerative colitis (UC) patients before and during the development of ileal pouchitis engraft and promote colitis in specific pathogen free (SPF) IL-10 deficient (IL-10-/-) mice, but not in wild type SPF mice or when mono-associated in germ free mice. The colitis in IL-10-/- mice was also associated with significant alterations in commensal microbiota potentially important for maintaining intestinal and immune homeostasis. UC pouchitis B. fragilis also engrafts in DSS-induced colitis in WT SPF mice, indicating a fitness advantage under conditions of mucosal inflammation over other commensals in the gut microbiota. These findings show that gut inflammation promotes the expansion and fitness of UC-derived Bacteroides species that is associated with changes in the SPF gut microbiota and may be promote colitis in genetically susceptible hosts. ImportanceThis study supports the notion that human inflammatory bowel diseases arise from the emergence of indigenous pathobionts in genetically-prone subjects. Colitis-promoting pathobionts are well-suited to establish themselves in the host inflammatory environment and outcompete endogenous microbiota. Once engrafted, the pathobiont can further aggravate inflammation in a genetically-susceptible host. Such complex interplay among several factors creates a vicious pro-inflammatory cycle and promotes disease development. These findings are consistent with our previous clinical observation that B. fragilis, an otherwise low-abundance commensal species, expands prior to the development of UC pouchitis. We believe these findings are relevant to the pathogenesis of UC pouchitis and possibly human inflammatory bowel diseases in general, underscoring the role of commensal to pathobiont transitions, rather than classical pathogens, in promoting and exacerbating the onset of human IBD.

microbiology↗

bioRxiv2023

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods

Peptide hormones are genome-encoded signal transduction molecules released in multicellular organisms. The dysregulation of hormone release can cause multiple health problems and it is crucial to study these hormones for therapeutic purposes. To help the research community working in this field, we developed a prediction server that classifies hormonal peptides and non-hormonal peptides. The dataset used in this study was collected for both plants and animals from Hmrbase2 and PeptideAtlas databases. It comprises non-redundant 1174 hormonal and 1174 non-hormonal peptide sequences which were combined and divided into 80% training and 20% validation sets. We extracted a wide variety of compositional features from these sequences to develop various Machine Learning (ML) and Deep Learning (DL) models. The best performing model was logistic regression model trained on top 50 features which achieved an AUROC of 0.93. To enhance the performance of ML model, we applied Basic Local Alignment Search Tool (BLAST) to identify hormonal sequences using similarity among them, and motif search using Motif-Emerging and Classes-Identification (MERCI) to detect motifs present in hormonal and non-hormonal sequences. We combined our best performing classification model, i.e., logistic regression model with BLAST and MERCI to form a hybrid model that can predict hormonal peptide sequences accurately. The hybrid model is able to achieve an AUROC of 0.96, an accuracy of 89.79%, and an MCC of 0.8 on the validation set. This hybrid model has been incorporated on the publicly available website of HOPPred at https://webs.iiitd.edu.in/raghava/hoppred/.

bioinformatics↗

bioRxiv2023

Hmrbase2: A comprehensive database of hormones and their receptors

Background and objectiveHormones are essential for cell communication and hence regulate various physiological processes. The discrepancies in the hormones or their receptors can break this communication and cause major endocrinological disorders. It is, therefore, indispensable for the therapeutics and diagnostics of hormonal diseases. MethodsWe collected widespread information on peptide and non-peptide hormones and hormone receptors. The information was collected from HMDB, UniProt, HORDB, ENDONET, PubChem and literature. ResultsHmrbase2 is an updated version of Hmrbase. The current version contains a total of 12056 entries which is more than twice the entries in the previous version. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties like melting point; water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been provided. Additionally, a similarity search module has been incorporated, enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. ConclusionsTo make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Homebase 2.0 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2.

bioinformatics↗

bioRxiv2023

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Identification of secretory proteins in body fluids is one of the key challenges in the development of non-invasive diagnostics. It has been shown in the part that a significant number of proteins are secreted by cells via exosomes called exosomal proteins. In this study, an attempt has been made to build a model that can predict exosomal proteins with high precision. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method BLAST was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning based models have been developed using compositional features of proteins and achieved highest AUROC of 0.70. The performance of the ML-based models improved significantly to AUROC of 0.73 when evolutionary information in the form of PSSM profiles was used for building models. Our analysis indicates that exosomal proteins have wide range of motifs. In addition, it was observed that exosomal proteins contain different types of sequence-based motifs, which can be used for predicting exosomal proteins. Finally, a hybrid method has been developed that combines a motif-based approach and an ML-based model for predicting exosomal proteins, achieving a maximum AUROC 0.85 and MCC of 0.56 on an independent dataset. The hybrid model in this study performs better than the presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred has been created for the scientific community to provide service, code, and data. (https://webs.iiitd.edu.in/raghava/exopropred/). KeypointsO_LIExosomal proteins or non-classical secretory proteins are secreted by via exosomes C_LIO_LIA method has been developed for predicting exosomal proteins C_LIO_LIModels have been trained, tested, and evaluated on non-redundant dataset C_LIO_LIWide range of sequence motifs have been discovered in exosomal proteins C_LIO_LIA web server and standalone software have been developed C_LI

bioinformatics↗

bioRxiv2022

IS200/IS605 Family-Associated TnpB Increases Transposon Activity and Retention

The IS200/IS605 family of insertion sequences are abundant mobile elements associated with one of the most numerous genes found in nature, tnpB1-3. Previous studies suggest that TnpB protein may be an evolutionary precursor to CRISPR Cas enzymes, and TnpB has received renewed interest having itself been shown to function as a Cas-like RNA-guided DNA endonuclease3,4. However, interpretation of the fundamental role of TnpB in transposition and how it contributes to genome dynamics5 remains controversial without direct, real-time measurement in live cells. Here, using a suite of fluorescent reporters coupled to transposition in live Escherichia coli, we show that IS608-TnpB causes increased transposon activity, and assists in preventing transposon loss from host genomes. Analyzing our results through a mathematical model of transposon dynamics, we discuss the multifaceted roles it may play in transposon regulation. The mutually beneficial transposon-TnpB interaction may explain the prevalence of tnpB, creating conditions for the appropriation of TnpBs RNA-guided endonuclease activity for adaptive immunity. SIGNIFICANCE STATEMENTPhylogenetic evidence suggests that tnpB, one of the most numerous genes found in nature, is the ancestral form of CRISPR-Cas enzymes and played a critical role in the evolution of adaptive immunity. However, the role TnpB plays in transposition that has contributed to its wide distribution remains unclear. Here, we use a unique approach that couples fluorescent reporters to transposition to non-perturbatively quantify transpositional dynamics in live cells. In contrast to previous indirect methods suggesting that TnpB suppresses transposition, our results instead clearly demonstrate that TnpB significantly increases transposition rates and enhances transposon retention within the host genome, resulting in a mutually beneficial interaction between transposons and TnpB that can account for its wide distribution.

synthetic biology↗

bioRxiv2022

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Adventitious rooting is critical to the propagation, breeding, and genetic engineering or editing of trees. The capacity for plants to undergo these processes is highly heritable; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of these processes, we performed a genome-wide association study (GWAS) using 1,148 genotypes of Populus trichocarpa. GWAS are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics, and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, and reactive oxygen species signaling. Genes related to other processes with known roles in root development, and numerous genes with uncharacterized functions and/or cryptic roles, were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of adventitious rooting.

plant biology↗

bioRxiv2022

GWAS identifies candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation, and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. While association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants, the power of these methods relies on the accuracy and scale of phenotypic data used. To enable a largescale GWAS of in planta regeneration in model tree Populus, we implemented a workflow involving semantic segmentation to quantify regenerating plant tissues (callus and shoot) over time. We found the resulting statistics are of highly non-normal distributions, which necessitated transformations or permutations to avoid violating assumptions of linear models used in GWAS. While transformations can lead to a loss of statistical power, we demonstrate that this can be mitigated by the application of the Augmented Rank Truncation method, or avoided altogether using the Multi-Threaded Monte Carlo SNP-set (Sequence) Kernel Association Test to compute empirical p-values in GWAS. We report over 200 statistically supported candidate genes, with top candidates including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. We demonstrate that sensitive genetic discovery for complex developmental traits can be enabled by a workflow based on computer vision and adaptation of several statistical approaches necessitated by to the complexity of regeneration trait expression and distribution.

plant biology↗

bioRxiv2022

Robust high-throughput phenotyping with deep segmentation enabled by a web-based annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physio-logical traits is limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale at low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study in the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of humans unassisted to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

plant biology↗

bioRxiv2022

Target-dependent siRNA suppression distinguishes self from non-self endogenous siRNAs in C. elegans germline

Despite their prominent role in transposon silencing, expression of endo-siRNAs is not limited to the "non-self" DNA elements. Transcripts of protein-coding genes ("self" DNA) in some cases also produce endo-siRNAs in yeast, plants, and animals [1]. How cells distinguish these two populations of siRNAs to prevent unwanted silencing of self-genes in animals is not well understood. To address this question, we examined the expression of ectopic siRNAs from an LTR retrotransposon in C. elegans germline. We found that the abundance of ectopic siRNAs was dependent on their homologous target genes: ectopic siRNAs against genes expressed only in somatic cells can be abundantly expressed. In contrast, ectopic siRNAs against germline-expressed genes are often suppressed. This phenomenon, which we termed "target-directed siRNA suppression", is dependent on the target mRNA and requires germline P-granule components. We found that siRNA suppression can also occur to naturally produced endo-siRNAs. We suggest that siRNA suppression plays an important role in regulating siRNA expression and preventing self-genes from aberrant epigenetic silencing.

molecular biology↗

bioRxiv2021

Transcriptomic analysis of ribosome biogenesis and pre-rRNA processing during growth stress in Entamoeba histolytica

Ribosome biogenesis, a multi-step process involving the transcription, modification, folding and processing of rRNA is the major consumer of cellular energy. It involves the sequential assembly of ribosomal proteins (RP)s via more than 200 ribogenesis factors. Unlike model organisms where transcription of rRNA and RP genes slows down during stress, in Entamoeba histolytica, pre-rRNA synthesis continues, and unprocessed pre-rRNA accumulates. To gain insight into the vast repertoire of ribosome biogenesis factors and understand the major components playing role during stress we computationally identified the ribosome biogenesis factors in E. histolytica. Of the total [~]279 S. cerevisiae proteins, we could only find 188 proteins in E. histolytica. Some of the proteins missing in E. histolytica were also missing in humans. A number of proteins represented by multiple genes in S. cerevisiae had only a single copy in E. histolytica. It was interesting to note that E. histolytica lacked mitochondrial ribosome biogenesis factors and had far less RNase components as compared to S. cerevisiae. Northern hybridization using probes from different spacer regions depicted the accumulation of unprocessed intermediates during stress. Transcriptomic studies revealed the differential regulation of a number of ribosomal factors both in serum-starved and RRP6KD conditions. The ARB1 protein involved at multiple steps of ribosome biogenesis and NEP1 and TSR3 involved in chemical modification of 18S rRNA previously shown to accumulate pre-rRNA precursors upon downregulation in S. cerevisiae and humans were included. The data reveals the importance of some of the major factors required for regulating pre-rRNA processing during stress. This is the first report on the complete repertoire of ribosome biogenesis factors in E. histolytica.

microbiology↗

bioRxiv2021

Targeted resurrection of chromosomal arm 1RS in two elite wheat lines with 1BL/1RS translocation for improved end-use quality

1BL/1RS translocation is widely used around the world to enhance wheat yield potential, resistance to various diseases, and adaptation. However, the translocation is combined with inherent quality problems associated with reduced dough strength and dough stickiness due to the presence of Sec-1 on proximal end and absence of GluB3/GliB1 on distal end. Two NILs, one carrying the distal (1RSRW) and the other carrying the proximal (1RSWR) fragment from 1BS, in background of Pavon were used for transferring these two loci in yellow rust resistant version of two elite wheat varieties PBW550+Yr5 and DBW17+Yr5. Foreground and background marker assisted selection was done for the Sec-1- and GluB3+ alongwith Lr26/Yr9/Sr31, Pm8 and 1RS loci in the advancing generation. BC2F5:6 NILs with absence of Secalin and presence of GluB3/GliB1 loci were evaluated for two years in replicated yield trial. A positive correlation of thousand grain weight (TGW), harvest index (HI), and tiller number per meter (TNpM) with yield (YD) with significant GxE effect was observed. Further multivariate analysis of these traits contributed maximum to the effective yield. Thirty promising NILs were identified with Sec-1-/GluB3+ alongwith with high yield contributing parameters.

genetics↗

bioRxiv2021

In silico model for predicting IL-2 inducing peptides in human

Interleukin-2 (IL-2) based immunotherapy has been approved for treating certain types of cancer, as IL-2 plays a crucial role in regulating the immune system. In this study, we developed a method for predicting IL-2-inducing peptides. Our method was trained, tested, and validated on a main dataset containing 6,574 experimentally validated Major histocompatibility complex (MHC) binders, including 3,429 IL-2-inducing and 3,145 non-inducing peptides. A primary analysis of IL-2 inducing and non-inducing peptides revealed that certain residues, such as alanine and leucine, are more abundant in IL-2-inducing peptides. Initially, we developed alignment-based methods, which demonstrated high precision but limited coverage. Subsequently, we developed artificial intelligence-based models, including machine learning (ML), deep learning (DL), and large language models (LLM), to predict IL-2-inducing peptides. Our Extra Tree-based model, developed using dipeptide composition and peptide length, achieved a maximum AUC of 0.82. Finally, we constructed ensemble models that combined artificial intelligence and alignment-based methods. Our best ensemble model, which integrates the Extra Tree-based model with MERCI, achieved the highest AUC of 0.84 and an MCC of 0.51 on the main dataset. One limitation of the main dataset is that both IL-2-inducing and non-inducing peptides are MHC binders. To address this limitation, we created two additional datasets: Alternate Dataset 1, consisting of 3,429 IL-2-inducing peptides and 3,429 non-inducing peptides (MHC non-binders), and Alternate Dataset 2, consisting of 3,429 IL-2-inducing peptides and 3,439 non-inducing peptides (MHC binders + MHC non-binders). Our best ensemble model achieved AUCs of 0.9 and 0.8 with MCCs of 0.61 and 0.44 on Alternate Datasets 1 and 2, respectively. To assist the scientific community, we have integrated the best models from this study into a standalone software and web server, IL2pred, which enables users to predict, scan, and design IL-2-inducing peptides (https://webs.iiitd.edu.in/raghava/il2pred/). HighlightsO_LIDevelopment of alignment-based techniques for predicting IL-2-inducing peptides. C_LIO_LIIdentification of amino acid patterns associated with IL-2 induction. C_LIO_LIImplementation of machine learning, deep learning, and LLM-based models for peptide prediction. C_LIO_LIEnsemble approaches combining alignment-based and AI-driven methods and integration into the webserver. C_LI

bioinformatics↗

bioRxiv2021

Proton egress pathway during the S1 to S2 transition of the Oxygen Evolving Complex ofPhotosystem II

Photosystem II uses water as the ultimate electron source of the photosynthetic electron transfer chain. Water is oxidized to dioxygen at the Oxygen Evolving Complex (OEC), a Mn4CaO5 inorganic core embedded in the lumenal side of PSII. Water-filled channels are thought to bring in substrate water molecules to the OEC, remove the substrate protons to the lumen, and may transport the product oxygen. Three water-filled channels, denoted large, narrow, and broad, that extend from the OEC towards the aqueous surface more than 15 [A] away are seen. However, the actual mechanisms of water supply to the OEC, the removal of protons to the lumen and diffusion of oxygen away from the OEC have yet to be established. Here, we combine Molecular Dynamics (MD), Multi Conformation Continuum Electrostatics (MCCE) and Network Analysis to compare and contrast the three potential proton transfer paths during the S1 to S2 transition of the OEC. Hydrogen bond network analysis shows that the three channels are highly interconnected with similar energetics for hydronium as calculated for all paths near the OEC. The channels diverge as they approach the lumen, with the water chain in the broad channel better interconnected that in the narrow and large channels, where disruptions in the network are observed at about 10 [A] from the OEC. In addition, the barrier for hydronium translocation is lower in the broad channel, suggesting that a proton from the OEC could access the paths near the OEC, and likely exit to the lumen via the broad channel, passing through PsbO.

biophysics↗

bioRxiv2020

Prognostic Biomarkers for Predicting Papillary Thyroid Carcinoma Patients at High Risk Using Nine Genes of Apoptotic Pathway

ObjectivesAberrant expression of apoptotic genes has been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. Materials and methodsIn this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. ResultsIt was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models. Our voting-based model achieved highest performance (HR=41.59, p=3.36x10-4, C=0.84, logrank-p=3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR=57.04 with p=10-4 (C=0.88, logrank-p=1.44x10-9). We also developed classification models that can classify high risk patients (survival [≤] 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Since these genes can also be used as potential therapeutic targets in PTC, we identified potential drug molecules which could modulate their expression profile. ConclusionThis study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

bioinformatics↗