Biology subjects

Raza, K.

Publications and source records attributed to Raza, K..

16 recordsLinked to original sources

bioRxiv2025

Inflammatory disease microbiomes share a functional pathogenicity predicted by C-reactive protein

We examine disease-specific and cross-disease functions of the human gut microbiome by colonizing germ-free mice, at risk for inflammatory arthritis, colitis, or neuroinflammation, with over 100 human fecal microbiomes from subjects with rheumatoid arthritis, ankylosing spondylitis, multiple sclerosis, ulcerative colitis, Crohns disease, or colorectal cancer. We find common inflammatory phenotypes driven by microbiomes from individuals with intestinal inflammation or inflammatory arthritis, as well as distinct functions specific to microbiomes from multiple sclerosis patients. Inflammatory disease in mice colonized with human microbiomes correlated with systemic inflammation, measured by C-reactive protein, in the human donors. These cross-disease patterns of human microbiome pathogenicity mirror features of the inflammatory diseases, including therapeutic targets and the presence or absence of systemic inflammation, suggesting shared and disease-specific mechanisms by which the microbiome is shaped and drives pathogenic inflammatory responses.

immunology↗

bioRxiv2024

Spatial programming of fibroblasts promotes resolution of tissue inflammation through immune cell exclusion

The role of fibroblasts in determining tissue topography and immune cell organisation within chronically inflamed tissues is poorly understood. Herein, we use multi-omic spatial analysis to define the cellular zonation pattern of the synovium in patients with inflammatory arthritis, identifying discrete tissue niches underpinned by spatially programmed subsets of synovial fibroblasts. We observe that perivascular fibroblasts switch on distinct matrix programs in response to cytokine signalling from neighbouring cells, forming adapted tissue niches that either permit or restrict immune cell trafficking. Specifically, IFN-{gamma}-responsive fibroblasts form a pathogenic lymphocyte-permissive niche that supports the persistence of leukocytes in the tissue, whilst TGF-{beta}-responsive, matrix-synthesising fibroblasts comprise a reparative niche, composed of a collagen-rich barrier around blood vessels that restricts leukocyte migration and promotes resolution of tissue inflammation. Augmentation of such endogenous pathways to promote resolution of inflammation may offer therapeutically tractable approaches for restoration of tissue homeostasis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/614064v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1e6e4dborg.highwire.dtl.DTLVardef@1baee04org.highwire.dtl.DTLVardef@1608a1forg.highwire.dtl.DTLVardef@10c1edb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

immunology↗

bioRxiv2022

SARS-CoV-2 variants show a gradual declining pathogenicity and pro-inflammatory cytokine stimulation and an increasing antigenic and anti-inflammatory cytokine induction

Hyper-transmissibility with decreased disease severity are typical characteristics of Omicron variant. To understand this phenomenon, we used various bioinformatics approaches to analyze randomly selected genome sequences (one each) of the Gamma, Delta, and Omicron variants submitted to NCBI from 15 to 31 December 2021. We show that: (i) Pathogenicity of SARS-CoV-2 variants decreases in the order: Wuhan > Gamma > Delta > Omicron; however, the antigenic property follows the order: Omicron > Gamma > Wuhan > Delta. (ii) Omicron Spike RBD has lower pathogenicity but higher antigenicity than other variants. (iii) Decreased disease severity by Omicron variant may be due to its decreased pro-inflammatory and IL-6 stimulation and increased IFN-{gamma} and IL-4 induction efficacy. (iv) Mutations in N protein are associated with decreased IL-6 induction and human DDX21-mediated increased IL-4 production in Omicron. (v) Due to mutations, the stability of S, M, N, and E proteins decreases in the order: Omicron > Gamma > Delta > Wuhan. (vi) Stronger Spike RBD-hACE2 binding in Omicron is associated with increased transmissibility. However, the lowest stability of the Omicron Spike protein makes Spike RBD-hACE2 interaction weak for systemic infection and for causing severe disease. Finally (vii), the highest instability of Omicron E protein may also be associated with decreased viral maturation and low viral load leading to less severe disease and faster recovery. Our method may be used for other similar viruses, and these findings will contribute to the understanding of the dynamics of SARS-CoV-2 variants and the management of emerging variants.

bioinformatics↗

bioRxiv2021

Glycan engineering using synthetic ManNAc analogues enhances sialyl-Lewis-X expression and adhesion of leukocytes

Sialyl-Lewis-X (sLeX/CD15s) epitopes regulate cell adhesion via interactions with selectins. High levels of sLeX are associated with chronic inflammation. Intense efforts have been devoted for the development of sLeX mimetics for the prevention of atherosclerosis. By contrast, low levels of sLeX are associated with leukocyte adhesion deficiency (LAD) disorders. In this context, we employed metabolic glycan engineering to alter the fine structures of sialoglycans. Treatment of HL-60 (human acute myeloid leukemia) cells with peracetyl N-cyclobutanoyl-D-mannosamine (Ac4ManNCb) resulted in a four-fold increase in both sLeX levels and adhesion to E-selectin-Fc chimera. Enhanced sLeX levels on CD162/PSGL-1, CD43, and CD44 were observed through immunoprecipitation. Molecular dynamics (MD) simulations on interactions with E-selectin revealed dramatic differentials in the conformational dynamics of sLeX-Cb compared to sLeX, highlighting the significance of remote N-acyl side chains of sialic acids in facilitating bio-active conformations. These results provide opportunities for pharmacological interventions in both LAD and chronic inflammation. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/473788v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@172ab54org.highwire.dtl.DTLVardef@14e45acorg.highwire.dtl.DTLVardef@1c05c65org.highwire.dtl.DTLVardef@1f2056e_HPS_FORMAT_FIGEXP M_FIG C_FIG Remote substituents on the tetrasaccharide sialyl-Lewis X (sLeX / CD15s) influence ensembles of bio-active conformations and alter biological outcomes. Treatment with peracetyl N-cyclobutanoyl-D-mannosamine (Ac4ManNCb) results in four-fold enhancement in the expression of sialyl-Lewis-X (sLeX / CD15s) and adhesion to E-selectin (CD62E). Molecular dynamics simulations show that the sLeX-Cb prefers a flatter bio-active conformation (gray) compared to wild-type sLeX (cyan). Institute and/or researcher Twitter usernames: @NImmunology @Gops_GlycoIndia

biochemistry↗

bioRxiv2021

BESFA: Bioinformatics based Evolutionary, Structural & Functional Analysis of Prostrate, Placenta, Ovary, Testis, and Embryo (POTE) Paralogs

The POTE family comprises 14 paralogues and is primarily expressed in Prostrate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins structure, function, and Adaptive Divergence. Our result discerns that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this studys outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies.

cancer biology↗

bioRxiv2021

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases

Pro-inflammatory fibroblasts are critical to pathogenesis in rheumatoid arthritis, inflammatory bowel disease, interstitial lung disease, and Sjogrens syndrome, and represent a novel therapeutic target for chronic inflammatory disease. However, the heterogeneity of fibroblast phenotypes, exacerbated by the lack of a common cross-tissue taxonomy, has limited the understanding of which pathways are shared by multiple diseases. To investigate, we profiled patient-derived fibroblasts from inflamed and non-inflamed synovium, intestine, lung, and salivary glands with single-cell RNA-sequencing. We integrated all fibroblasts into a multi-tissue atlas to characterize shared and tissue-specific phenotypes. Two shared clusters, CXCL10+CCL19+ immune-interacting and SPARC+COL3A1+ vascular-interacting fibroblasts were expanded in all inflamed tissues and additionally mapped to dermal analogues in a public atopic dermatitis atlas. We further confirmed these human pro-inflammatory fibroblasts in animal models of lung, joint, and intestinal inflammation. This work represents the first cross-tissue, single-cell fibroblast atlas revealing shared pathogenic activation states across four chronic inflammatory diseases.

genomics↗

bioRxiv2020

Relevance of enriched expression of SARS-CoV-2 binding receptor ACE2 in gastrointestinal tissue with pathogenesis of digestive symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 patients

IntroductionCOVID-19 is caused by a new strain of coronavirus called SARS-coronavirus-2 (SARS-CoV-2), which is a positive sense single strand RNA virus. In humans, it binds to angiotensin converting enzyme 2 (ACE2) with the help a structural protein on its surface called the S-spike. Further, cleavage of the viral spike protein (S) by the proteases like transmembrane serine protease 2 (TMPRSS2) or Cathepsin L (CTSL) is essential to effectuate host cell membrane fusion and virus infectivity. COVID-19 poses intriguing issues with imperative relevance to clinicians. The pathogenesis of GI symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 are of particular relevance because they cannot be sufficiently explained from the existing knowledge of the viral diseases. Tissue specific variations of SARS-CoV-2 cell entry related receptors expression in healthy individuals can help in understanding the pathophysiological basis the aforementioned collection of symptoms. Materials and MethodsThe data were downloaded from the Human Protein Atlas available at (https://www.proteinatlas.org/humanproteome/sars-cov-2) and the tissue specific expressions (both mRNA and protein) of ACE2 and TMPRSS2 as yielded from the studies with RNA sequencing and immunohistochemistry (IHC) were analyzed as a function of the various components of the digestive tract. A digestive system specific functional enrichment map of ACE2 gene was created using g:profiler (https://biit.cs.ut.ee/gprofiler/gost) utility and the data were visualized using Cytoscape software, version 3.7.2 (https://cytoscape.org/). ResultsThe correlated expression (transcriptomic and proteomic) of ACE2 (to which SARS-CoV-2 binds through the S-spike) was found to be enriched in the lower gastrointestinal tract (GIT) (highest in small intestine, followed by colon and rectum), and was undetectable in the upper GIT components: mouth cavity (tongue, oral mucosa, and salivary glands), esophagus, and stomach. High expression of ACE2 was noted in the glandular cells as well as in the enterocytes in the lining epithelium (including brush border epithelium). Among other digestive system organs, Gall bladder (GB) showed high expression of ACE2 in glandular cells, while any protein expression was undetectable in liver and pancreas. TMPRSS2 was found enhanced in GIT and exocrine glands of pancreas, and co-localized with ACE2 in enterocytes. ConclusionsBased on the findings of this study and supportive evidence from the literature we propose that a SARS-CoV-2 binding with ACE2 mediates dysregulation of the sodium dependent nutrient transporters and hence may be a plausible basis for the digestive symptoms in COVID-19 patients. ACE2 mediated dysregulation of sodium dependent glucose transporter (SGLT1 or SLC5A1) in the intestinal epithelium also links it to the pathogenesis of diabetes mellitus which can be a possible reason for the associated mortality in COVID-19 patients with diabetes. High expression of ACE2 in mucosal cells of the intestine and GB make these organs potential sites for the virus entry and replication. Continued replication of the virus at these ACE2 enriched sites may be a basis for the disease recurrence reported in some, thought to be cured, patients. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

pathology↗

bioRxiv2020

Loss of α2-6 sialylation promotes the transformation of synovial fibroblasts into a pro-inflammatory phenotype in Rheumatoid Arthritis.

In healthy joints, synovial fibroblasts (SFs) provide the microenvironment required to mediate homeostasis but are recognized to adopt a pathological role in rheumatoid arthritis (RA), promoting the infiltration and activation of immune cells to perpetuate local inflammation, pain and joint destruction. Carbohydrates (glycans) attached to cell surface proteins are fundamental regulators of cellular interactions between stromal and immune cells, but very little is known about the glycome of SFs or how glycosylation regulates their biology. Here we fill these gaps in our understanding of stromal guided pathophysiology by systematically mapping glycosylation pathways in healthy and arthritic SFs. We used a combination of transcriptomic and glycomic analysis to show that transformation of fibroblasts into pro-inflammatory cells in RA is associated with profound glycan remodeling, a process that involves reduction of 2-6 terminal sialylation that is mostly mediated by TNF-dependent inhibition of the glycosyltransferase ST6Gal1. We also show that sialylation of SFs correlates with distinct disease stages and SFs functional subsets in both human RA and models of mouse arthritis. We propose that pro-inflammatory cytokines in the joint remodel the SF-glycome, transforming a regulatory tissue intended to preserve local homeostasis, into an under-sialylated and highly pro-inflammatory microenvironment that contributes to an amplificatory inflammatory network that perpetuates chronic inflammation. These results highlight the importance of cell glycosylation in stromal immunology.

immunology↗

bioRxiv2018

Differential Expression Analysis of ZIKV Infected Human RNA Sequence Reveals Potential Biomarkers

Zika virus (ZIKV) is considered to be an emerging viral outbreak due to its link to diseases like microcephaly, Guillain-Barre Syndrome in human. In this paper, we identify differentially expressed genes (DEGs) using RNA-seq data. In this study, we adopted the RNA-seq analysis pipeline to quantify RNA-seq data into read counts. Our analysis uncovers the significant DEGs which may be involved in the altered biological process somehow. Here, we report the list of significant DEGs, out of which three genes are found to be highly differentially expressed. In addition, our analysis also predicts other moderate DEGs, low DEGs whose differential expression was induced due to ZIKV infections.

bioinformatics↗

bioRxiv2018

Localisation and hypersecretion of Nerve Growth Factor in breast Phyllodes tumours: evidence from a preliminary study

BackgroundThe pathophysiology of the breast phyllodes tumours is uncertain. Currently, wide surgical removal is the only available treatment option. The histopathological diagnosis of phyllodes tumours is often confused with that of fibroadenomas due to a striking histological resemblance; hence a distinctive biomarker for this tumour type is warranted.\n\nMaterial & MethodsFresh human breast tissue was obtained from surgically excised breast phyllodes and fibroadenoma tumours (test, 2 cases each), breast cancer (positive control, 2 cases) and normal breast tissue (negative control, 1 case). Immunohistochemistry was performed for the detection of nerve growth factor (NGF) on frozen sections of the test and control tissues fixed in 4% paraformaldehyde, using the indirect streptavidin-biotin-peroxidase complex method. Sandwich ELISA on tissue homogenates of the same test and control cases was also performed to validate the immunohistochemical findings.\n\nResultsA marked difference in NGF expression was detected in phyllodes tumours compared to fibroadenomas. The maximum NGF expression was observed in phyllodes tissue followed by cancer tissue, and the least expression in fibroadenomas (3-5 times less than in phyllodes; comparable with normal breast tissue).\n\nConclusionNGF is known for its growth inducing potential in breast cancer, but its secretion by a benign breast tumour is not known in literature. This study reports abundant NGF secretion by breast phyllodes, raising the possibility of its potential role in tumour pathogenesis and progression that can be exploited therapeutically in future. We also propose that NGF may be used as a distinct biomarker of phyllodes tumours, for differentiating them from fibroadenomas during histopathology.

cancer biology↗

bioRxiv2017

Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

Gene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

systems biology↗

bioRxiv2017

Protein Features Identification For Machine Learning-Based Prediction Of Protein-Protein Interactions

The long awaited challenge of post-genomic era and systems biology research is computational prediction of protein-protein interactions (PPIs) that ultimately lead to protein functions prediction. The important research questions is how protein complexes with known sequence and structure be used to identify and classify protein binding sites, and how to infer knowledge from these classification such as predicting PPIs of proteins with unknown sequence and structure. Several machine learning techniques have been applied for the prediction of PPIs, but the accuracy of their prediction wholly depends on the number of features being used for training. In this paper, we have performed a survey of protein features used for the prediction of PPIs. The open research challenges and opportunities in the area have also been discussed.

bioinformatics↗

bioRxiv2017

Mathematical Model For Plant-Insect Interaction With Dynamic Response To PAD4-BIK1 Interaction And Effect Of BIK1 Inhibition

Plant-insect interaction system has been a widely studied model of the ecosystem. Attempts have long been made to understand the numerical behaviour of this counter system and make improvements in it from initial simple analogy based approach with predator-prey model to the recently developed mathematical interpretation of plant-insect interaction including concept of plant immune interventions Caughley and Lawton (1981). In our current work, we propose an improvement in the model, based on molecular interactions behind plant defense mechanism and its effect on the plant growth and insect herbivory. Motivated from an interaction network of plant biomolecules given by Louis and Shah (2014) and extending the model of Chattopadhyay, et al (2001), we propose here a mathematical model to show how plant insect interaction system is governed by the molecular components inside. Insect infestation mediated induction of Botrytis Induced Kinase-1 (BIK-1) protein causes inhibition of Phyto Alexin Deficient-4 (PAD4) protein. Lowered PAD4, being responsible for initiating plant defense mechanism, results in degraded plant immune potential and thus causes loss of plant quality. We adapt these interactions in our model to show how they influence the plant insect interaction system and also to reveal how silencing BIK-1 may aid in enhanced production of plant biomass by increasing plant immunity mediated by increase in PAD4 and associated antixenotic effects. We hypothesize the significance of BIK-1 inhibition which could result in the improvement of the plant quality. We explain the interaction system in BIK-1 inhibition using mathematical model. Further, we adopted the plethora of computational modeling and simulations techniques to identify the mechanisms of molecular inhibition.

systems biology↗

bioRxiv2017

Altered Expression Of A Unique Set Of Genes Reveals Complex Etiology Of Schizophrenia

PurposeThe etiology of schizophrenia is extensively debated, and multiple factors have been contended to be involved. A panoramic view of the contributing factors in a genome-wide study can be an effective strategy to provide a comprehensive understanding of its causality.\n\nMaterials and MethodsGSE53987 dataset downloaded from GEO-database, which comprised mRNA expression data of post-mortem brain tissue across three regions from control and age-matched subjects of schizophrenia (N= Hippocampus (HIP): C-15, T-18, Prefrontal cortex (PFC): C-15, T-19, Associative striatum (STR): C-18, T-18). Bio-conductor-affy-package used to compute mRNA expression, and further t-test applied to investigate differential gene expression. The analysis of the derived genes performed using PANTHER Classification System and NCBI database.\n\nResultsA set of 40 genes showed significantly altered (p<0.01) expression across all three brain regions. The analyses unraveled genes implicated in biological processes and events, and molecular pathways relating basic neuronal functions.\n\nConclusionsThe deviant expression of genes maintaining basic cell machinery explains compromised neuronal processing in SCZ.\n\nAbbreviationsSchizophrenia (SCZ), Hippocampus (HIP), Associative striatum (STR), Prefrontal cortex (PFC)

neuroscience↗

bioRxiv2017

Multiple Kernel Learning Approach For Medical Image Analysis

Computer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high dimensional and heterogeneous data. In this chapter, after presenting a breif description about nature of medical images, image features and basics in machine learning and kernel methods, we present the application of multiple kernel learning algorithms for medical image analysis.

bioinformatics↗

bioRxiv2017

Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data

RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.

bioinformatics↗