Biology subjects

Asif, A.

Publications and source records attributed to Asif, A..

4 recordsLinked to original sources

bioRxiv2026

A DMT1-dependent iron-endoplasmic reticulum-extracellular matrix axis regulates cancer cell invasion

Intracellular iron homeostasis is increasingly recognized as a regulator of cancer cell behavior, but how iron distribution influences extracellular matrix (ECM) organization and invasion remains poorly understood. Here, we show that loss of divalent metal transporter 1 (DMT1/SLC11A2) disrupts intracellular iron homeostasis and promotes cancer cell invasion through an iron-ER-ECM axis. In MDA-MB-231 cells, DMT1 knockout (KO) reduced total iron content but increased the labile iron pool (LIP) in both 2D and 3D culture models, indicating altered intracellular iron distribution. Across transcriptomic and phenotypic readouts, DMT1-dependent effects were more evident in 3D than in 2D models, with DMT1 KO inducing endoplasmic reticulum (ER) stress and impaired collagen/ECM organization. Functionally, the DMT1-loss phenotype was marked by reduced 2D motility, whereas in 3D spheroid models DMT1 KO cells displayed enhanced invasive outgrowth in both Matrigel and collagen matrices. Iron chelation further modulated this phenotype in a DMT1-dependent manner. Pharmacologic induction of ER stress phenocopied the loose spheroid architecture and invasive behavior, supporting ER stress as a mechanistic link between altered iron handling and ECM destabilization. Together, these findings identify intracellular iron distribution, rather than total iron abundance alone, as a determinant of ECM integrity and context-dependent cancer cell invasion. One Sentence Summary: Iron misrouting, not iron excess, drives ER stress and collagen failure that unleash invasion in triple-negative breast cancer.

cancer biology↗

bioRxiv2025

A Human Single-cell Atlas Identifies OLR1+ Scar-associated Macrophages as a Therapeutic Target for Chronic Liver Disease

Chronic liver disease (CLD) is a major global healthcare problem. Irrespective of cause, chronic damage to the liver results in fibrosis, which is associated with adverse clinical outcomes. Immune cells, in particular monocyte-derived macrophages (MDMs), are key regulators of fibrosis and represent an attractive therapeutic target for CLD. However, it has remained unclear which specific subpopulation of MDMs drives pro-inflammatory and pro-fibrotic functions in human CLD and how they might be selectively modulated. Here, we generate an annotated human liver single cell atlas from 42 healthy and 35 CLD patients, identifying 125 transcriptionally distinct cellular states. Leveraging large patient and cell numbers, our atlas resolves rare liver cell states and distinguishes two types of disease-expanded scar-associated macrophages (SAMac), including a specific subpopulation with a pro-inflammatory pro-fibrotic phenotype. The scavenger receptor OLR1 was enriched in pro-inflammatory SAMacs and high hepatic OLR1 expression was associated with increased mortality in CLD patients. A corresponding monocyte-derived OLR1+ SAMac subpopulation expanded in a mouse model of CLD and exhibited a pro-inflammatory phenotype based on single-cell RNA-seq, single-cell ATAC-seq, and flow cytometry analyses. Primary human OLR1+ MDMs promoted fibrogenic signalling in multilineage liver spheroid cultures, whilst specific targeting of OLR1 reduced IL-1{beta} production by human macrophages and attenuated myofibroblast activation. Overall, our annotated human liver single-cell atlas provides a valuable reference to study disease-associated cell states in CLD. We utilise this resource to identify a distinct pro-inflammatory subpopulation of SAMacs and highlight OLR1 as a potential therapeutic target to specifically modulate SAMac function and attenuate liver fibrosis.

immunology↗

bioRxiv2020

An assessment of efficacy of Iodine complex (Renessans) against SARS-CoV-2 in non-human primates (Rhesus macaque)

Renessans is an iodine complex which has proven in vitro antiviral activity including Anti-SARS-CoV-2 activity. The present study was designed to determine its efficacy against SARS-CoV-2 in monkeys (Rhesus macaque). A total of 14 monkeys were divided into four groups: A) Prophylactic group (n=03), (B) Treatment group (n=03), (C) infection control group (n=04) and (D) negative control group (n=04) and were housed in BSL-3 Animal facility while group D was housed at another animal house. Group A was administered with Renessans @ 2.85 mg/7 kg from 5 days prior to the infection to 08 days post infections (DPI). Group B was administered with Renessans from 03-08 DPI @ 2.85 mg/7 kg. Group C was administered with WIF only. The infection @ 2 x 106 TCID of SARS-CoV-2 was given to all group monkeys through intranasal and oral route under anesthesia. Nasal swab samples (at different times) and fecal matter on daily basis were collected for the detection of SARS-CoV-2 through real-time quantitative PCR. Three monkeys (one from each of group A, B and C) were euthanized at 07 DPI to determine the gross pathological lesions and SARS-CoV-2 detection from internal tissues. Nasal swabs from all the monkeys from group A, B and C were positive for SARS-CoV-2 at 02 and 07 DPI (Day 05 of treatment). At 14 DPI, all (100%) nasal swabs from group A were negative for SARS-CoV-2 while 50% and 100% were positive from group B and C, respectively. At 21 DPI, monkeys from group B were negative and all in group C were still positive for SARS-CoV-2. Similarly, fecal matter of monkeys in group A and B was returned negative in significantly lesser time as compared to monkeys from infection control group. Based on these research findings it is concluded that the Renessans has in-vivo SARS-CoV-2 activity and may result in early clearance of SARS-CoV-2. Therefore, a clinical trial of the drug in COVID-19 patients may reveal its anti-COVID-19 potential.

microbiology↗

bioRxiv2019

Machine Learning Predicts New Anti-CRISPR Proteins

The increasing use of CRISPR-Cas9 in medicine, agriculture and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many such anti-CRISPRs have been found in mobile genetic elements that disable the CRISPR-Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties that can be used for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method for identifying new potential anti-CRISPRs directly from proteomes using protein sequence information only. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking and extensively benchmarked it through non-redundant cross-validation and external validation. We then applied AcRanker to predict candidate anti-CRISPRs from self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA16 (ML1) and AcrIIA17 (ML8). We show that AcrIIA16 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA17 inhibits both SpyCas9 and SauCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

biochemistry↗