Biology subjects

Malik, M. Z.

Publications and source records attributed to Malik, M. Z..

15 recordsLinked to original sources

bioRxiv2026

Elucidation of putative key genes involved in the regulation of triple negative breast cancer development and progression

The molecular basis of triple-negative breast cancer (TNBC), a highly aggressive and therapy-resistant subtype of breast cancer, is poorly understood. This study aims to identify key genes and pathways involved in TNBC development and progression using a systems biology approach followed by experimental validation. Here, two transcriptome microarray datasets from the GEO database were analysed using the R package LIMMA to detect differentially expressed genes (DEGs) in TNBC tumors. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses using the DAVID database were performed to identify DEGs regulated biological functions and pathways. Further, a protein-protein interaction (PPI) network was constructed using the STRING online database, and the topological properties were determined using MCODE and Cytohubba plug-ins. The expression and the prognostic value of the hub genes were validated using the Cancer Genome Atlas (TCGA) survival analysis. We found 727 DEGs, of which 473 were downregulated and 254 were upregulated in TNBC vs. non-TNBC samples. The GO and KEGG analyses indicated that the DEGs were mainly related to cell adhesion, tumorigenesis, and cellular immunity. The PPI network had shown six hub genes, namely CCND1, CDH1, ESR1, FN1, IL6, and PPARG, as the top key regulators. All these genes were validated by quantitative real-time PCR in the TNBC cell line using non-TNBC cell line as a calibrator, and the obtained results were in accordance with the bioinformatics data. This information may contribute to understanding the various molecular mechanisms that drive the development and progression of TNBC tumors.

cancer biology↗

bioRxiv2025

FAVABEAN and FALAPhyl: Open-Source Pipelines for Scalable 16s rRNA Microbiome Data Processing and Visualization

Reproducible and scalable analysis of 16S rRNA amplicon sequencing data remains a persistent challenge in microbiome research due to the complexity of available tools, incompatibilities between platforms, and the need for extensive bioinformatics expertise. We developed two containerized workflows--FAVABEAN (Fast Amplicon Variant Annotation, Binning, Error-correction And ANalysis) and FALAPhyl (Forays into Automating Laborious Analyses of Phylogeny)--to address these challenges. FAVABEAN and FALAPhyl are Snakemake-based pipelines designed for flexible execution across local, cluster, and cloud environments. FAVABEAN automates preprocessing, ASV inference, and taxonomic assignment using DADA2 and FIGARO, including primer averaging when samples are sequenced with multiple primers. FALAPhyl supports downstream analysis including alpha/beta diversity, network analysis, and differential abundance testing, with integrated provenance tracking. We validated both pipelines using three case studies involving oral microbiome datasets. In Case Study 1, we compared oral microbiota across family members and niches, showing primer-dependent variability in ASV-based similarity and minimal reseeding from familial sources after prophylaxis. Case Study 2 analyzed dental aerosol samples, revealing no significant microbial differences between pre-, intra-, and post-procedure air. Case Study 3, a randomized trial of a nitrate mouthrinse, demonstrated no significant microbiome shifts, highlighting oral microbial stability. FALAPhyls integration of DAtest enabled empirical evaluation of multiple statistical tests, aiding robust differential abundance inference. FAVABEAN and FALAPhyl offer a reproducible, automated solution for 16S rRNA amplicon data analysis. Their modular design, containerization, and provenance tracking enhance accessibility and scientific rigor in microbiome research.

bioinformatics↗

bioRxiv2025

siRNA-based Therapeutic Candidate Targeting PRDM2 for Inhibition of Lung Cancer Progression

Lung cancer is the leading cause of tumor-related fatalities worldwide. The current precision therapies are effective but target only those molecules that are impacted in a minority of lung cancer patients. Positive Regulatory Domain 2 (PRDM2), a member of the PRDM gene family, also known as Retinoblastoma Interacting Zinc finger (RIZ) protein. RIZ is found to be misregulated in more than 60% of lung cancer patients, representing a significantly affected population when compared to other mutation-prone oncogenic factors. RIZ expresses two contrasting variants, a tumor suppressor RIZ1, and an oncogenic RIZ2 protein. In lung cancer, the RIZ1/RIZ2 ratio alters, and the epigenetic silencing of RIZ1 promotes RIZ2 overexpression. In this study, we used siRNA (ARIZ-047) to knockdown and inhibit the effect of the oncogenic protein RIZ2. Inhibiting RIZ2 with ARIZ-047 treatment increased RIZ1 expression and decreased the viability of a lung cancer cell line (A549). The ARIZ-047 treatment also upregulated the negative regulators of the Wnt signaling pathway and restrained the tumor progression in the xenograft mice model. In conclusion, this study identifies ARIZ-047 as a targeted therapy in lung cancer, which specifically inhibits RIZ2 and suggests the role of the Wnt signaling pathway in RIZ2 overexpression. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/636957v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1a5eb34org.highwire.dtl.DTLVardef@1f4dc2forg.highwire.dtl.DTLVardef@a3ce29org.highwire.dtl.DTLVardef@33f576_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Clonal spheroids capture functional and genetic heterogeneity of head and neck cancer

Head and neck cancer squamous cell carcinoma (HNSCC) cells exhibit both structural and functional diversity, making them valuable models for understanding tumor heterogeneity at clinical levels. In this study, we generated single-cell-derived spheroids (SCDS) from HNSCC cell lines and patient tumor cells using scaffold- and non-scaffold-based methods to assess this variability. A distinct structural variability among these SCDS, categorized as hypo- and hyperproliferative spheroids based on size, was observed. Hyperproliferative spheroids demonstrated heightened proliferative and tumorigenic potential and increased sensitivity to cisplatin and radiation, while hypoproliferative spheroids exhibited enhanced migratory capabilities. Single-cell RNA sequencing (scRNA-seq) of hypo- and hyperproliferative spheroids provided insights into the transcriptional landscape of HNSCC cells, validating the observed structural and functional heterogeneities within primary tumors. These functionally and genetically characterized spheroids offer valuable tools for the development of next-generation therapeutics. Statement of SignificanceEstablishment and characterization of single-cell-derived spheroids from head and neck cancer cells, employing scaffold and non-scaffold materials, demonstrate functional and genetic heterogeneity. Single-cell analysis reveals correlations between genetic diversity and spheroid functionality. These characterized spheroids offer potential for advancing therapeutics development.

cancer biology↗

bioRxiv2023

Distinct composition of plasma virome in HIV-infected subjects on antiretroviral therapy compared to controls

Microbiome study during HIV infection is widely documented with major emphasis on bacteriome, while virome studies are few. The alteration of plasma virome is reported in HIV patients who are either treatment naive or have a weak immune system. Less is known about the plasma virome in HIV-infected patients on ART with preserved CD4 counts. In our pilot study, viral DNA isolated from plasma was sequenced on Illumina Nextseq500 platform. With the help of VIROMATCH pipeline, we observed that the plasma virome of HIV patients were significantly distinct from controls on the basis of Bray-Curtis dissimilarity index. The species, Human gammaherpesvirus 4 and families, Herpesviridae and Siphoviridae were found to be significantly enriched and differentially abundant in HIV patients. Hence, plasma virome is an important component for future study that might influence disease progression among HIV patients during therapy.

microbiology↗

bioRxiv2020

Troxerutin acts on complement mediated inflammation to ameliorate arthritic symptoms in rats.

Troxerutin (TXR), is a phytochemical reported to possess anti-inflammatory and hepatoprotective effects. In this study, we aimed to exploit anti-arthritic properties of TXR using an adjuvant induced arthritic (AIA) rat model. AIA induced rats showed highest arthritis score at disease onset and by oral administration of TXR (50, 100, 200 mg/kg body weight), reduced to basal level in a dose dependent manner. Isobaric tag for relative and absolute quantitative (iTRAQ) proteomics tool was employed to identify deregulated joint homogenate proteins in AIA and TXR treated rats to decipher probable mechanism of the TXR action in arthritis. iTRAQ analysis identified a set of 434 joint homogenate proteins with 65 deregulated proteins (log2 case/control [≥]1.5) in AIA. Expressions of a set of important proteins (AAT, T-kininogen, vimentin, desmin, and nucleophosmin) that could classify AIA from healthy were validated using Western blot analysis. Western blot data corroborated proteomics findings. In silico protein-protein interaction study of joint homogenate proteome revealed that complement component 9, the major building blocks of the membrane attack complex (MAC) responsible for sterile inflammation, gets perturbed in AIA. Our dosimetry study suggests that a TXR dose of 200 mg/kg body weight for 15 days is sufficient to bring the arthritis score to basal levels in AIA rats. We have shown the importance of TXR as an anti-arthritis agent in AIA model and after additional investigation its arthritis ameliorating properties could be exploited for clinical usability.

biochemistry↗

bioRxiv2020

Role of selective Bioactive Compounds as an Angiotensin Converting Enzyme Inhibitor

Hypertension is one of a major reason of mortality and morbidity and it is associated with heart and renal disease. The aim of this study is to find out the antihypertensive role of bioactive compounds from selected medicinal plants targeting ACE molecule which so far is not known. The plants taken in this study were Moringa oleifera, Azadirachta indica, and Hibiscus sabdariffa. The nitric oxide and superoxide scavenging property vary from 39.50% to 68% and 37.67 % to 75.50 %. respectively. The inhibition of ACE activity was found maximally in methanolic extract of A. indica (74 %), followed by H. sabdariffa (73.4%), and least in M. oleifera (71.8 %). The bioactive chloroform fraction was characterized for the presence of compound using standard techniques such as LCMS and NMR (13C-NMR 1H-NMR). The results revealed the presence of beta-sitosterol in M. oleifera, azadiradionolide in A. indica and hibiscitrin in H. sabdariffa. The compounds have shown significant low binding energy for hibiscitrin (-12.3kcal/mol), beta-sitosterol (-11.2kcal/mol) and azadiradionolide (-11.3kcal/mol) indicating the high efficacy of binding on the enzyme. While, binding energy of drug captopril was -5.6kcal/mol & enalpril - 8.1kcal/mol in the same pocket of the ACE molecule. Upon subjecting molecular dynamic simulation results indicated that beta sitosterol complex provided more compactness than the hibiscitrin and azadiradionolide compounds. The current study delivers a new perspective for the drug development against systolic blood pressure regulation and also opens new horizons for considering alternate highly potent drug target for hypertension.

bioinformatics↗

bioRxiv2020

Multi-Omics and Integrated Network Approach to Unveil Evolutionary Patterns, Mutational Hotspots, Functional Crosstalk and Regulatory Interactions in SARS-CoV-2

SARS-CoV-2 pandemic resulted in 92 million cases in a span of one year. The study focuses on understanding population specific variations attributing its high rate of infections in specific geographical regions particularly in USA. Rigorous phylogenomic network analysis of complete SARS-CoV-2 genomes (245) inferred five central clades named a (ancestral), b, c, d and e (subtype e1 & e2). The clade d & e2 were found exclusively comprising of USA. Clades were distinguished by 10 co-mutational combinations in Nsp3, ORF8, Nsp13, S, Nsp12, Nsp2 and Nsp6. Our analysis revealed that only 67.46% of SNP mutations were at amino acid level. T1103P mutation in Nsp3 was predicted to increase protein stability in 238 strains except 6 strains which were marked as ancestral type; whereas co-mutation (P409L & Y446C) in Nsp13 were found in 64 genomes from USA highlighting its 100% co-occurrence. Docking highlighted mutation (D614G) caused reduction in binding of Spike proteins with ACE2, but it also showed better interaction with TMPRSS2 receptor contributing to high transmissibility among USA strains. We also found host proteins, MYO5A, MYO5B, MYO5C had maximum interaction with viral proteins (N, S, M). Thus, blocking the internalization pathway by inhibiting MYO5 proteins which could be an effective target for COVID-19 treatment. The functional annotations of the HPI network were found to be closely associated with hypoxia and thrombotic conditions confirming the vulnerability and severity of infection. We also screened CpG islands in Nsp1 & N conferring ability of SARS-CoV-2 to enter and trigger ZAP activity inside host cell. ImportanceIn the current study we presented a global view of mutational pattern observed in SARS-CoV-2 virus transmission. This provided a who-infect-whom geographical model since the early pandemic. This is hitherto the most comprehensive comparative genomics analysis of full-length genomes for co-mutations at different geographical regions specially in USA strains. Compositional structural biology results suggested that mutations have balance of contrary forces effect on pathogenicity suggesting only few mutations to effective at translation level but not all. Novel HPI analysis and CpG predictions elucidates the proof of concept of hypoxia and thrombotic conditions in several patients. Thus, the current study focuses the understanding of population specific variations attributing high rate of SARS-CoV-2 infections in specific geographical regions which may eventually be vital for the most severely affected countries and regions for sharp development of custom-made vindication strategies.

microbiology↗

bioRxiv2020

Complexity in SARS-CoV-2 genome data: Price theory of mutant isolates

SARS-CoV-2 is a highly virulent and deadly RNA virus causing the Covid-19 pandemic and several deaths across the world. The pandemic is so fast that any concrete theory of sudden widespread of this disease is still not known. In this work, we studied and analyzed a large number of publicly available SARS-CoV-2 genomes across the world using the multifractal approach. The mutation events in the isolates obey the Markov process and exhibit very high mutational rates, which occur in six specific genes and highest in orf1ab gene, leading to virulent nature. f () analysis indicated that the isolates are highly asymmetric (left-skewed), revealing the richness of complexity and dominance by large fluctuations in genome structure organization. The values of Hq and Dq are found to be significantly large, showing heterogeneous genome structure self-organization, strong positive correlation in organizing the isolates, and quite sensitive to fluctuations in and around it. We then present multiple-isolates hosts-virus interaction models, and derived Price equation for the model. The phase plane analysis of the model showed asymptotic stability type bifurcation. The competition among the mutant isolates drives the trade-off of the dominant mutant isolates, otherwise confined to the present hosts.

systems biology↗

bioRxiv2020

ACE2 Homo-dimerization, Human Genomic variants and Interaction of Host Proteins Explain High Population Specific Differences in Outcomes of COVID19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive single-stranded RNA virus that causes a highly contagious Corona Virus Disease (COVID19). Entry of SARS-CoV-2 in human cells depends on binding of the viral spike (S) proteins to cellular receptor Angiotensin-converting enzyme 2 (ACE2) and on S-protein priming by host cell serine protease TMPRSS2. Recently, COVID19 has been declared pandemic by World Health Organization (WHO) yet high differences in disease outcomes across countries have been seen. We provide evidences to explain these population-level differences. One of the key factors of entry of the virus in host cells presumably is because of differential interaction of viral proteins with host cell proteins due to different genetic backgrounds. Based on our findings, we conclude that a higher expression of ACE2 is facilitated by natural variations, acting as Expression quantitative trait loci (eQTLs), with different frequencies in different populations. We suggest that high expression of ACE2 results in homo-dimerization, proving disadvantageous for TMPRSS2 mediated cleavage of ACE2; whereas, the monomeric ACE2 has higher preferential binding with SARS-CoV-2 S-Protein vis-a-vis its dimerized counterpart. Further, eQTLs in TMPRSS2 and natural structural variations in the gene may also result in differential outcomes towards priming of viral S-protein, a critical step for entry of the Virus in host cells. In addition, we suggest that several key host genes, like SLC6A19, ADAM17, RPS6, HNRNPA1, SUMO1, NACA, BTF3 and some other proteases as Cathepsins, might have a critical role. To conclude, understanding population specific differences in these genes may help in developing appropriate management strategies for COVID19 with better therapeutic interventions.

genetics↗

bioRxiv2019

Pathological circadian rhythm states driven by CK2 and noise

Circadian rhythm maintains sleep-wake cycle in living systems. Disruption of this rhythm may cause diseases. We propose an extended Drosophila circadian rhythm model incorporating cross-talk of CK2 with Per protein. We studied the model using stochastic simulation algorithm, and the behavior of the amplitude, time period and permutation entropy us identify three distinct circadian states namely, active, weak activity, active, weak activity and rhythmic death all driven by CK2. These states may correspond to distinct pathological cellular states of the living system. Noise, an important factor, has ability to switch normal circadian rhythm to any of the three aforementioned circadian states. Fluctuations in systems size, can help us in deterning the extent of noise present. We also highlighted that disruption in circadian rhythm may lead to various diseases including cancer. We present various cellular pathways driven by per mutant genes and their pathological states.\n\nStatement of significanceCircadian rhythm, which is one of the most important biological rhythm, regulates and intervenes various cellular processes. Significant changes in the rhythmic dynamics may lead to pathological states which may trigger various diseases. In this work, the impact of CK2 via per gene mutants on rhythmic dynamics is investigated, and found three distinct states, namely, active, weak activity and rhythmic death driven by CK2 which may correspond to various cellular states. Noise due to intrinsic random molecular events and cellular size variability is found to have the capability of regulating and controlling rhythmic properties, and can trigger to the three rhythmic states. We then listed various possible pathways which are regulated by per gene mutants and corresponding various possible pathological states.

systems biology↗

bioRxiv2019

Identification of key regulators in Prostate cancer from gene expression datasets of patients

Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k[≥]65) were selected from the PCa network, traced, and 19 of them were identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

systems biology↗

bioRxiv2019

Interplay of cellular states: Role of delay as control mechanism

Delay is everywhere, no matter how small or big it is. Experimental evidences show the existence and importance of time delayed reactions specially in biological systems. The role of delay is found to be multifunctional and is seemed to be system dependent. The analytically solved P(X, t) of gene regulatory process shows universal class of Poisson process at stationary condition. However, time delay creates a possible condition to the system to impart correlation in the stochastic process as sub-Poissonian or noise enhancement process which could be important in regulating and controlling the system. The results of simulation of few biological systems (gene regulation, circadian rhythm, and repressilator) using delay stochastic simulation algorithm show the possibilities of delay induced onset of oscillating states, which could be the active states of the systems, where, the system can establish coherence among the system variables, enhance the signal processing, optimize the system activities, etc. On the other hand, delay can also induce switching off of the oscillating states, which may correspond to inactive state or system failure. Further, delay can also create coherent bistable states of the system variables, at which the system can stay longer to make decisions about the fate of the system.

systems biology↗

bioRxiv2019

Organization in complex brain networks: energy distributions and phase shift

The Hamiltonian function of a network, derived from the intrinsic distributions of nodes and edges, magnified by resolution parameter has information on the distribution of energy in the network. In brain networks, the Hamiltonian function follows hierarchical features reflecting a power-law behavior which can be a signature of self-organization. Further, the transition of three distinct phases driven by resolution parameter is observed which could correspond to various important brain states. This resolution parameter could thus reflect a key parameter that controls and balances the energy distribution in the brain network.

neuroscience↗

bioRxiv2018

Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

BackgroundTuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker.\n\nObjectivesWe want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body.\n\nResultsWe identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathways importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression.\n\nConclusionsThis study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.

systems biology↗