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Kaur, H.

Publications and source records attributed to Kaur, H..

At least 73 records · Page 4Linked to original sources

Radiation Threats to Humans in Space and an alternative approach with Probiotics

Space type radiation is an important factor to consider for scientists on International Space Stations, especially high linear transfer energy (LET) since it has imminent effects on microorganisms. The abundances of bacteria are a good indicator of how radiation influences the gut microbiome. The current study is an attempt towards this; thus, we have employed a public dataset (Bioproject code PRJNA368790) of 80 mice samples treated with a range of doses from 0Gy to 1Gy and feces samples were collected at different time points of post radiation treatment. Metagenomic analysis was performed on this data to understand the effect of radiation doses on the abundance of microbial species or microbial diversity implementing the DADA2 and Phyloseq pipelines. Our analyses have shown that 0.1Gy high LET radiation had the significant effect on the species of bacteria. There is a significant decrease in four types of bacteria, i.e., Bifidobacterium longum, Bifidobacterium castoris, Lactobacillus gasseri and Lactobacillus johnsonii, with p-value of 7.05x10-5, 0.020, 0.057and 0.020, respectively. Additionally, pathway analysis indicates the protein coding products of these bacteria are involved in the GABAergic synaptic pathway. Further, our study has shown the significant difference between post radiation time points, i.e., 10 days vs.30 days and suggested the acclimatization period could be around 10 days for these bacteria.

microbiology↗

Network Theory Reveals Principles of Spliceosome Structure and Dynamics

Cryo-EM has revolutionized structural biology of the spliceosome and dozens of distinct spliceosome structures representing much of the splicing cycle have now been determined. However, comparison of these structures is challenging due to extreme compositional and conformational dynamics of the splicing machinery and the thousands of intermolecular interactions created or dismantled as splicing progresses. We have used network theory to quantitatively analyze the dynamic interactions of splicing factors throughout the splicing cycle by constructing structure-based networks from every protein-protein, protein-RNA, and RNA-RNA interaction found in eight different spliceosome structures. Our networks reveal that structural modules comprising the spliceosome are highly dynamic with factors oscillating between modules during each stage along with large changes in the algebraic connectivities of the networks. Overall, the spliceosomes connectivity is focused on the active site in part due to contributions from non-globular proteins and components of the NTC. Many key components of the spliceosome including Prp8 and the U2 snRNA exhibit large shifts in both eigenvector and betweenness centralities during splicing. Other factors show transiently high betweenness centralities only at certain stages thereby suggesting mechanisms for regulating splicing by briefly bridging otherwise poorly connected network nodes. These observations provide insights into the organizing principles of spliceosome architecture and provide a framework for comparative network analysis of similarly complex and dynamic macromolecular machines.

biophysics↗

Integrative multi-omics approach for stratification of tumor recurrence risk groups of Hepatocellular Carcinoma patients

Postoperative tumor recurrence is one of the major concerns associated with the poor prognosis of HCC patients. There is yet to elucidate a standard surveillance system for HCC recurrence risk owing to complexity of this malignancy. Generation of multi-omics data from patients facilitate the identification of robust signatures for various diseases. Thus, the current study is an attempt to develop the prognostic models employing multi-omics data to significantly (p-value <0.05) stratify the recurrence high-risk (median Recurrence Free Survival time (RFS) =<12 months) and low-risk groups (median RFS >12 months). First, we identified key 90RNA, 50miRNA and 50 methylation features and developed prognostic models; attained reasonable performance (C-Index >0.70, HR >2.5), on training and validation datasets. Subsequently, we developed a prognostic (PI) model by integrating the four multi-omics features (SUZ12, hsa-mir-3936, cg18465072, and cg22852503), that are biologically inter-linked with each other. This model achieved reasonable performance on training and validation dataset, i.e. C-Index 0.72, HR of 2.37 (1.61 - 3.50), p-value of 6.72E-06, Brier score 0.19 on training dataset, and C-Index 0.72 (95% CI: 0.63 - 0.80), HR of 2.37 (95% CI: 1.61 - 3.50), p-value of 0.015, Brier score 0.19 on validation dataset. Eventually, Drugbank data was investigated to elucidate therapeutic potential of these signatures. We have identified nine potential drugs against three genes (CA9, IL1A, KCNJ15) that are positively correlated with the tumor recurrence. We anticipate these results from our study will help researchers and clinicians to improve the HCC recurrence surveillance, eventually outcome of patients.

bioinformatics↗

In silico validation and fabrication of matrix diffusion based polymeric transdermal films for repurposing gabapentin hydrochloride in oncogenic neuropathic pain.

BackgroundGabapentin (GBP) is an FDA approved drug for the treatment of partial and secondary generalized seizures, apart from also being used for diabetic neuropathy. GBP displays highly intricate mechanism of action and its inhibitory response in elevated antagonism of NMDA (N-methyl-D-aspartate receptor) receptor and has potential in controlling neuropathic pain of cancer origin. ObjectiveTherefore, in the present study, we have selected BCATc (Pyridoxal 5-phosphate dependent branched-chain aminotransferase cytosolic) enzyme that is highly expressed in neuropathic stress conditions and have analysed the GBP as its competitive inhibitor by modeling, docking and checking its pharmacokinetic suitability through ADMET. Though in this study the results exhibited higher efficacy of GBP in controlling neuropathic pain, the drug shows certain potential therapeutic limitations like shorter half-life, repetitive dosing, high inter subjective variability. MethodsTherefore, a suitable and equally efficacious drug delivery method was also designed and developed by loading GBP transdermal patches (GBP-TDP) by solvent evaporation method using PVP and HPMC in ratio of 2:1 as a polymer base for reservoir type of TDP. Also, PEG 400 was used as a plasticizer and PVA (4%) was taken for backing membrane preparation and then the optimized GBP-TDP was subjected for physical characterization, optimization and ex vivo release kinetics. Results and conclusionThe results showed desired specifications with uneven and flaky surface appearance giving avenue for controlled release of the drugs with 75.58% of drug release in 12 hrs., further suggesting that GBP-TDP can be used as an effective tool against diabetic neuropathy pain.

neuroscience↗

Determination of the amount of insecticide picked up by mosquitoes from treated nets

BackgroundInsecticides used in vector control mostly rely on vectors being exposed through contact with treated surfaces, yet little is known about the amount picked up by the insect. Measuring this amount is relevant not only for determining the actual doses that are lethal to the mosquito, but also for understanding effects on the physiology and vector competence of mosquitoes. Insecticides at sub-lethal doses can affect both parasites developing inside mosquitoes and mosquito microbiota, hence it is important to understand the processes by which parasites are exposed to insecticide inside the insect. These doses will inevitably depend on the amount of insecticide that mosquitoes pick up when they come into contact with treated nets. MethodsThree to five days old non-blood fed female Anopheles coluzzii mosquitoes were exposed to a long-lasting insecticidal net (PermaNet 2.0 containing 55 mg/m2 deltamethrin), using a wire ball frame, for 0.5-5.0 minutes. Our in-house developed colorimetric test was used to visually detect the amount of deltamethrin on different parts of the mosquito (legs, heads, thoraxes, abdomens) following exposure to the net. The amount of insecticide picked up by mosquitoes from the net over a range of exposure times was measured using a high-performance liquid chromatography with diode array detection (HPLC-DAD). ResultsThe colorimetric test, designed to only detect the type 2 pyrethroids (i.e deltamethrin, -cypermethrin and {lambda}-cyhalothrin) on fabrics (e.g. ITNs) and sprayed walls, was successfully used for the first time to detect deltamethrin on mosquitoes following exposure to the net. The confirmatory HPLC-DAD analysis determined that after 2 min exposure up to 12 ng of deltamethrin adhered to mosquitoes following exposure to PermaNet 2.0 (mean = 5.2 ng/mosquito, SE = 1.9) and that the final dose depends on the length of exposure time. ConclusionsThis study demonstrated the potential of a screening (type 2 pyrethroid colorimetric test) and a confirmatory test (HPLC-DAD) to determine the amount of insecticide that adheres to mosquitoes on contact with treated surfaces. This has implications for a precise lethal dose determination and detection of specific insecticide that causes the greatest mosquito mortality in circumstances where mixtures of insecticides may be used to maximise effectiveness of interventions.

biochemistry↗

Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth

While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWAS), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of comprehensive functional genomic annotations in human cell types relevant to pregnancy and PTB. Here, we generated extensive transcriptional and chromatin annotations of cultured primary decidua-derived mesenchymal stromal/stem cells (MSCs) and in vitro differentiated decidual stromal cells (DSCs) and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women. This resulted in a significant enrichment of heritability estimates in functional noncoding regions in stromal cells, as well as in the discovery of additional loci associated with gestational duration and target genes of associated loci. Our strategy illustrates how systematic functional annotations in pregnancy-relevant cell types aid in the experimental follow-up of GWAS for PTB and, likely, other pregnancy-related conditions.

genomics↗

Impact of Cancer-Associated Mutations in Hsh155/SF3b1 HEAT Repeats 9-12 on pre-mRNA Splicing in Saccharomyces cerevisiae

Mutations in the splicing machinery have been implicated in a number of human diseases. Most notably, the U2 small nuclear ribonucleoprotein (snRNP) component SF3b1 has been found to be frequently mutated in blood cancers such as myelodysplastic syndromes (MDS). SF3b1 is a highly conserved HEAT repeat (HR)-containing protein and most of these blood cancer mutations cluster in a hot spot located in HR4-8. Recently, a second mutational hotspot has been identified in SF3b1 located in HR9-12 and is associated with acute myeloid leukemias, bladder urothelial carcinomas, and uterine corpus endometrial carcinomas. The consequences of these mutations on SF3b1 functions during splicing have not yet been tested. We incorporated the corresponding mutations into the yeast homolog of SF3b1 and tested their impact on splicing. We find that all of these HR9-12 mutations can support splicing in yeast, and this suggests that none of them are loss of function alleles in humans. The Hsh155V502F mutation alters splicing of several pre-mRNA reporters containing weak branch sites as well as a genetic interaction with Prp2 and physical interactions with Prp5 and Prp3. The ability of a single allele of Hsh155 to perturb interactions with multiple factors functioning at different stages of the splicing reaction suggests that some SF3b1-mutant disease phenotypes may have a complex origin on the spliceosome.

biochemistry↗

Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics

Human Leukocyte Antigen (HLA) is an essential component of the immune system which stimulates immune cells to provide protection and defense against cancer. More than thousands of HLA alleles have been reported in the literature; but, only a specific set of HLA alleles expressed in an individual. Recognition of cancer-associated mutations by the immune system depends on the presence of a particular set of alleles, that elicit an immune response to fight against cancer. It indicates that the occurrence of specific HLA alleles also affects the outcome of the cancer patients. In the current study, prediction models have been developed using 415 skin cutaneous melanoma (SKCM) patients for predicting the overall survival of patients from their HLA-alleles. It has been observed that, the presence of certain superalleles in the patients, is responsible for improved overall survival which were referred as favourable superalleles like HLA-B*55 (HR=0.15, 95% CI 0.034 to 0.67), HLA-A*01 (HR=0.5, 95% CI 0.3 to 0.8). In contrast, presence of certain superalleles in the patients is responsible for their poor survival, those superalleles were referred as unfavourable superalleles such as HLA-B*50 (HR=2.76, 95% CI 1.284 to 5.941), HLA-DRB1*12 (HR=3.44, 95% CI 1.64 to 7.2). We developed prediction models using 14 HLA-superalleles and five clinical characteristics for predicting high-risk SKCM patients and achieve HR=4.52 (95% CI 3.088-6.609) with p-value = 8.01E-15. Lastly, we provide a web-based service to community for predicting the risk in SKCM patients (https://webs.iiitd.edu.in/raghava/skcmhrp/)

bioinformatics↗

TP63, SOX2 and KLF5 Establish Core Regulatory Circuitry and Construct Cancer Specific Epigenome in Esophageal Squamous Cell Carcinoma

Transcriptional network is controlled by master transcription factors (TFs) and cis-regulatory elements through interacting with target sequences and recruiting epigenetic regulators. By integration of enhancer profiling and chromatin accessibility, we establish super-enhancer (SE) mediated core regulatory circuitry (CRC) for esophageal squamous cell carcinoma (ESCC) and identify tumor cells-dependent CRC TFs-TP63, SOX2 and KLF5. They preferentially co-occupy SE loci and form a positive interconnected auto-regulatory loop through SEs to orchestrate chromatin and transcriptional programming. SE-associated oncogene-ALDH3A1 is identified as a novel CRC target contributing to ESCC viability. Using circular chromosome conformation capture sequencing (4C-seq) and CRISPR/Cas9 genome editing, the direct interaction between TP63 promoter and functional enhancers which is mediated by CRC TFs is identified. Deletion of each enhancer decreases expression of CRC TFs and impairs cell viability, phenocopying the knockdown of each CRC TF. Targeting epigenetic regulation by inhibition of either the BET bromodomain or HDAC disrupts the CRC program and its dependent global epigenetic modification, consequently suppressing ESCC tumor growth. Importantly, combination of both compounds result in synergistic anti-tumor effect.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC=\"FIGDIR/small/825372v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (58K):\norg.highwire.dtl.DTLVardef@1a9f923org.highwire.dtl.DTLVardef@238c90org.highwire.dtl.DTLVardef@1e14f58org.highwire.dtl.DTLVardef@120249a_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LISuper-enhancers mediated transcriptional regulatory circuitry is established for ESCC\nC_LIO_LITP63, SOX2 and KLF5 as CRC TFs co-localize super-enhancer loci to orchestrate chromatin accessibility and transcriptional dysregulation\nC_LIO_LIComplex interaction between functional enhancers and TP63 promoter is mediated by CRC TFs\nC_LIO_LIALDH3A1 is a key downstream target of ESCC CRC and is essential for ESCC cell survival\nC_LIO_LIBET degrader-ARV-771 and HDAC inhibitor-Romidepsin synergistically inhibit ESCC tumor growth\nC_LI

cancer biology↗

GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

BackgroundIn past number of web-based resources has been developed in the field of Bioinformatics. These resources are heavily used by scientific community to provide solution for challenges faced by experimental researchers particularly in the field of biomedical sciences. There are number of challenges in utilizing full potential of these services that includes internet speed, limits on computing power, and security of data. In order to enhance utilities of these web-based assets, we developed a docker-based container that integrates large number resources available in literature.\n\nResultsThis paper describes GPSRdocker a docker-based container developed for providing wide-range of computational tools in the field of bioinformatics particularly in genomics, proteomics and system biology. Majority of tools integrated in GPSRdocker are based on web services developed at Raghavas group in last two decades. Broadly, these tools can be categorized in three categories; i) general scripts, ii) supporting software and iii) major standalone software. In order to facilitate students or developers working in the field of bioinformatics, we developed general scripts in Perl and Python. These general-purpose scripts serve as building block for any bioinformatics tools like computing features/descriptors of a protein. Supporting software packages includes SCIKIT, WEKA, SVMlight, and PSI-BLAST; these software packages allow one to develop/implement bioinformatics software. Major Standalone software is core of this container which allows predicting function/class of biomolecules. These tools can be classified broadly in following categories; protein annotation, epitope-based vaccines, prediction of interaction and drug discovery.\n\nConclusionA docker-based container has been developed which can be easily run on any operating system as well as it can be directly ported on cloud. Scripts can be run to build pipelines for addressing problems at system level like prediction of vaccine candidate for a pathogen. GPSRdocker including manual is available free for academic use from https://webs.iiitd.edu.in/gpsrdocker.

bioinformatics↗

Loss of dop-2 causes increased dopamine release and locomotory defects in the presence of ethanol

Ethanol is a widely used drug, excessive consumption of which could lead to medical conditions with diverse symptoms. Ethanol abuse causes disinhibition of memory, attention, speech and locomotion across species. Dopamine signaling plays an essential role in ethanol dependent behaviors in animals ranging from C. elegans to humans. We devised an ethanol dependent assay in which mutants in the dopamine autoreceptor, dop-2, displayed a unique sedative locomotory behavior causing the animals to move in circles while dragging the posterior half of their body. We identify the posterior dopaminergic sensory neuron as being essential to modulate this behavior. We further demonstrate that in dop-2 mutants, ethanol exposure increases dopamine secretion and results in enhanced function of the DVA interneuron. DVA releases the neuropeptide NLP-12 and leads to the excitation of cholinergic motor neurons that affect movement. Thus, DOP-2 modulates dopamine levels at the synapse and regulates alcohol induced movement through NLP-12.

neuroscience↗

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma using Large-scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on a small dataset obtained from a specific platform or lack of their reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms; we have employed large scale transcriptomic profiling datasets containing a total of 2,306 HCC and 1,655 non-tumorous tissue samples. These samples were obtained from 29 studies generated by mainly four types of profiling techniques include Affymetrix, Illumina, Agilent and High-throughput-seq, which implemented a wide range of platforms. Firstly, we scrutinized 26 genes that are differentially expressed or regulated in uniform pattern among numerous datasets. Subsequently, we identified three genes (FCN3, CLEC1B, & PRC1) panel-based HCC biomarker using different machine learning techniques include Simple-threshold based approach, Extra Trees, Support Vector Machine, Random Forest, K Neighbors Classifier, Logistic Regression etc. Three-genes panel-based HCC biomarker classified HCC samples and non-tumorous samples of training and three external validation datasets with an accuracy between 93 to 98% and AUROC (Area Under Receiver Operating Characteristic curve) in a range of 0.97 to 1.0. Furthermore, the prognostic potential of these genes was evaluated on TCGA cohort and GSE14520 cohort using univariate survival analysis revealed that these genes are independent prognostic indicators for various types of the survivals, i.e. OS (Overall Survival), PFS (Progression-Free Survival), DFS/RFS (Disease-Free Survival/Recurrence-Free Survival) and DSS (Disease-Specific Survival) of HCC patients and significantly stratify high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three genes-based biomarker that can predict HCC patients with high precision; also possess significant prognostic potential. Eventually, to provide service to the scientific community, we developed a webserver HCCPred based on the above study (http://webs.iiitd.edu.in/raghava/hccpred/).

bioinformatics↗

A RAD9-dependent cell cycle arrest in response to unresolved recombination intermediates in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, the conserved Sgs1-Top3-Rmi1 helicase-decatenase regulates homologous recombination by limiting accumulation of recombination intermediates that are precursors of crossovers. In vitro studies have suggested that the dissolution of double-Holliday junction joint molecules by Sgs1-driven convergent junction migration and Top3-Rmi1 mediated strand decatenation could be responsible for this. To ask if dissolution occurs in vivo, we conditionally depleted Sgs1 and/or Rmi1 during return to growth, a procedure where recombination intermediates formed during meiosis are resolved when cells resume the mitotic cell cycle. Sgs1 depletion during return to growth delayed joint molecule resolution, but ultimately most were resolved and cells divided normally. In contrast, Rmi1 depletion resulted in delayed and incomplete joint molecule resolution, and most cells did not divide. rad9{Delta} mutation restored cell division in Rmi1-depleted cells, indicating that the DNA damage checkpoint caused this cell cycle arrest. Restored cell division in rad9{Delta}, Rmi1-depleted cells frequently produced anucleate cells, consistent with the suggestion that persistent recombination intermediates prevented chromosome segregation. Our findings indicate that Sgs1-Top3-Rmi1 acts in vivo, as it does in vitro, to promote recombination intermediate resolution by dissolution. They also indicate that, in the absence of Top3-Rmi1 activity, unresolved recombination intermediates persist and activate the DNA damage response, which is usually thought to be activated by much earlier DNA damage-associated lesions.

genetics↗

Single Cell Analysis Reveals Partial Reactivation of X-chromosome Instead of chromosome wide dampening in Naive Human Pluripotent Stem Cells

Recently, a unique form of X-chromosome dosage compensation has been demonstrated in human preimplantation embryos, which happens through the dampening of X-linked gene expression from both X-chromosomes. Subsequently, X-chromosome dampening has also been demonstrated in female human pluripotent stem cells (hPSCs) during the transition from primed to naive state. However, the existence of dampened X-chromosomes remains controversial in both embryos and hPSCs. Specifically, in preimplantation embryos it has been shown that there is inactivation of X-chromosome instead of dampening. Here, we have performed allelic analysis of X-linked genes at the single cell level in hPSCs and found that there is partial reactivation of the inactive X-chromosome instead of chromosome-wide dampening upon conversion from primed to naive state. In addition, our analysis suggests that the reduced X-linked gene expression in naive hPSCs might be the consequence of erasure of active X-chromosome upregulation.

genetics↗

SAPdb: A database of nanostructures formed by self-assembly of short peptides

BackgroundNanostructures generated by self-assembly of peptides yield nanomaterial that has many therapeutic applications, including drug delivery and biomedical engineering, due to their low cytotoxicity and higher uptake by targeted cells owing to their high affinity and specificity towards cell surface receptors. Despite the promising implications of this rapidly expanding field, there is no dedicated resource to study peptide nanostructures. ResultThis study endeavours to create a dedicated repository of short peptides, which may prove to be the best models to study ordered nanostructures formed by peptide self-assembly. SAPdb has a repertoire of 1,049 entries of experimentally validated nanostructures formed by the self-assembling of small peptides. It includes 701 entries are of dipeptides, 328 entries belong to tripeptides, and 20 entries of single amino acid with some conjugated partners. Each entry encompasses comprehensive information about the peptide such as chemical modifications in the peptide sequences, the type of nanostructure formed, and experimental conditions like pH, temperature, and solvent required for the self-assembly of the peptide, etc. Further, our analysis has shown that the occurrence of aromatic amino acids favours the formation of self-assembling nanostructures, as indicated by a large number of entries in SAPdb contain aromatics amino acids. Besides, we have observed that these peptides form different nanostructures under different experimental conditions. SAPdb provides this comprehensive information in a hassle-free tabulated manner at a glance. User-friendly browsing, searching, and analysis modules are integrated for easy retrieval and comparison of data and examination of properties. We anticipate SAPdb to be a valuable repository for researchers engaged in the burgeoning arena of nanobiotechnology. AvailabilityThe database can be accessed on the web at https://webs.iiitd.edu.in/raghava/sapdb.

bioinformatics↗

Computing wide range of protein/peptide features from their sequence and structure

MotivationIn last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not suitable to predict function of a protein at residue level such as prediction of ligand binding residues, DNA interacting residues, post translational modification etc.\n\nResultsIn order to facilitate scientific community, we have developed a software package that computes more than 50,000 features, important for predicting function of a protein and its residues. It has five major modules for computing; composition-based features, binary profiles, evolutionary information, structure-based features and patterns. The composition-based module allows user to compute; i) simple compositions like amino acid, dipeptide, tripeptide; ii) Properties based compositions; iii) Repeats and distribution of amino acids; iv) Shannon entropy to measure the low complexity regions; iv) Miscellaneous compositions like pseudo amino acid, autocorrelation, conjoint triad, quasi-sequence order. Binary profile of amino acid sequences provides complete information including order of residues or type of residues; specifically, suitable to predict function of a protein at residue level. Pfeature allows one to compute evolutionary information-based features in form of PSSM profile generated using PSIBLAST. Structure based module allows computing structure-based features, specifically suitable to annotate chemically modified peptides/proteins. Pfeature also allows generating overlapping patterns and feature from whole protein or its parts (e.g., N-terminal, C-terminal). In summary, Pfeature comprises of almost all features used till now, for predicting function of a protein/peptide including its residues.\n\nAvailabilityIt is available in form of a web server, named as Pfeature (https://webs.iiitd.edu.in/raghava/pfeature/), as well as python library and standalone package (https://github.com/raghavagps/Pfeature) suitable for Windows, Ubuntu, Fedora, MacOS and Centos based operating system.

bioinformatics↗

S. cerevisiae Srs2 helicase ensures normal recombination intermediate metabolism during meiosis and prevents accumulation of Rad51 aggregates

We investigated the meiotic role of Srs2, a multi-functional DNA helicase/translocase that destabilizes Rad51-DNA filaments, and is thought to regulate strand invasion and prevent hyper-recombination during the mitotic cell cycle. We find that Srs2 activity is required for normal meiotic progression and spore viability. A significant fraction of srs2 mutant cells progress through both meiotic divisions without separating the bulk of their chromatin, although sister centromeres often separate. Undivided nuclei contain aggregates of Rad51 colocalized with the ssDNA-binding protein RPA, suggesting the presence of persistent single-strand DNA. Rad51 aggregate formation requires Spo11-induced DSBs, Rad51 strand-invasion activity, and progression past the pachytene stage of meiosis, but not the DSB end-resection or the bias towards inter-homologue strand invasion characteristic of normal meiosis. srs2 mutants also display altered meiotic recombination intermediate metabolism, revealed by defects in the formation of stable joint molecules. We suggest that Srs2, by limiting Rad51 accumulation on DNA, prevents the formation of aberrant recombination intermediates that otherwise would persist and interfere with normal chromosome segregation and nuclear division.

genetics↗

Expression based biomarkers and models to classify early and late stage samples of Papillary Thyroid Carcinoma

In this study, we describe the key transcripts and machine learning models developed for classifying the early and late stage samples of Papillary Thyroid Cancer (PTC), using transcripts expression data from The Cancer Genome Atlas (TCGA). First, we rank all the transcripts on the basis of area under receiver operating characteristic curve, (AUROC) value to discriminate the early and late stage, based on an expression threshold. With the expression of a single transcript DCN, we can classify the stage samples with a 68.5% accuracy and AUROC of 0.66. Then we implemented various combination of multiple gene panels, selected using various gold standard feature selection techniques. The model based on the expression of 36 multiple transcripts (protein coding and non-coding) selected using SVC-L1 achieves the maximum accuracy of 74.51% with AUROC of 0.75 on independent validation dataset with balanced sensitivity and specificity. Further, these signatures also performed well on external microarray data obtained from GEO, predicting nearly 70% (12 samples out of 17 samples) early stage samples correctly. Further, multiclass model, classifying the normal, early and late stage samples achieves the accuracy of 75.43% with AUROC of 0.80 on independent validation dataset. With correlation analysis, we found that transcripts with maximum change in correlation of their expression in both the stages are significantly enriched in neuroactive ligand receptor interaction pathway. We also propose a panel of five protein coding transcripts, which on the basis of their expression, can segregate cancer and normal samples with 97.32% accuracy and AUROC of 0.99 on independent validation dataset. All the models and dataset used in this study are available from the web server CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

bioinformatics↗