Biology subjects

Zaman, N.

Publications and source records attributed to Zaman, N..

7 recordsLinked to original sources

bioRxiv2026

Type 1 immunity linked to CMV exposure improves long-term survival in sepsis without shock

Dysregulated immune responses are at the core of sepsis pathology and can drive both acute organ dysfunction and long-term morbidity and mortality. To better understand the association between specific immune histories, perturbations and sepsis outcomes, we combined immune phenotyping, functional cytokine production assays and antibody repertoire profiling. While sepsis immune profiles were highly heterogeneous, we identified a strong association between the frequency of CXCR3-expressing CD4 memory T cells at time of enrollment and six-month survival in sepsis without shock. Surprisingly, the same population was linked to improved organ function trajectories in septic shock patients but did not influence six-month survival, highlighting the importance of considering physiological states to define immune predictors. Finally, phage display profiling revealed that these Th1 cells were generally expanded in CMV-seropositive individuals, suggesting that previous and latent infections may modulate sepsis outcomes.

immunology↗

bioRxiv2026

A Unified Agent-Enabled Platform for Drug Repurposing across Molecular, Phenotypic, and Clinical Scales

Drug repurposing offers an effective path to new therapies, yet existing computational approaches rely on a single line of evidence and are rarely validated across biological scales. We present LinkD, an integrated framework that unifies diffusion-based affinity prediction, proteome-wide selectivity scoring, phenotypic validation, and population-scale clinical evidence. LinkD-Bind predicts binding across 14,981 drugs and 20,385 human targets, ranking first in 8 of 9 BindingDB, Davis, and KIBA evaluations, with the largest gains under cold-start conditions. LinkD-Select recovers 95.3% of known drug-target pairs by combining selectivity scoring and molecular docking. LinkD-Pheno integrates drug-sensitivity and CRISPR dependency data across 960 cancer cell lines, identifying 34 novel drug-gene pairs and recovering [~]85% of known targets among the top 50 candidates. Across 11.5 million individuals from Mount Sinai and UK Biobank, LinkD-prioritized {beta}-blockers propranolol (HR 0.82) and carvedilol (HR 0.92) reduced 5-year prostate cancer incidence relative to metoprolol, corroborated by ADRB2 docking and LNCaP growth inhibition. LinkD-Agent, which can effectively orchestrate all evidence layers, is served on a publicly available web platform (https://linkd-agent.onrender.com/), enabling a wide range of users to derive new drug repurposing opportunities through natural language queries.

bioinformatics↗

bioRxiv2024

Development of the ULK1-Recruiting Chimeras (ULKRECs) to enable proximity-induced and ULK1-dependent degradation of mitochondria

Targeted protein degradation (TPD) has opened new opportunities to investigate signalling pathways as a research tool, and as a unique therapeutic strategy using bifunctional chimeric small molecules, with candidate molecules in clinical trials for the treatment of breast cancer and prostate cancer. Most current TPD approaches use the 26S proteasomal machinery via PROteolysis TArgeting Chimeras (PROTACs), however, new emerging strategies using the autophagy system, termed AUtophagy TArgeting Chimeras (AUTACs) expand on the degrader arsenal and repertoire of targets that can be degraded. This includes non-protein molecules such as lipid droplets, organelles, insoluble protein aggregates as well as typical TPD targets, soluble intracellular proteins. AUTACs were proposed to operate by binding the target of interest (TOI) and linking it to an autophagy cargo protein (LC3 or p62), tethering the TOI into forming autophagosomes. In this study, we designed an alternative strategy for AUTACs, reasoning that the local recruitment and activation of ULK1 is sufficient to induce the formation of an autophagosome at the site of recruitment. As a proof of concept, we used an ULK1 agonist linked to a mitochondrial targeting ligand and termed these chimeric molecules ULK1-Recruiting Chimeras (ULKRECs). We show that local activation of ULK1 by ULKRECs at the outer mitochondrial membrane (OMM) induces mitophagy, further enhanced by mitochondrial insult. Using Parkinsons disease (PD) patient-derived fibroblasts, we show the ULKRECs induce mitophagy independently of the PRKN/PINK axis, components required to signal for canonical mitophagy in response to stressors and often dysfunctional in many neurological diseases. We propose that ULKRECs are a novel class of degraders that have potential as unique therapeutics for diseases where dysfunctional mitophagy plays a key role in disease pathology and progression.

cell biology↗

bioRxiv2023

Comparative Analysis, Diversification and Functional Validation of Plant Nucleotide-Binding Site Domain Genes

Nucleotide-binding site (NBS) domain genes are one of a superfamily of resistance genes involved in plant responses to pathogens. The current study identified presumably identified 12,820 NBS-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes classified into 168 classes with several novel domain architectures patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBS etc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG0, OG1, OG2 etc.) and unique (highly specific to species; OG80, OG82 etc) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG2, OG6, and OG15 in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to CLCuD. The genetic variation between susceptible (Coker 312) and tolerant (Mac7) G. hirsutum accessions identified several unique variants in NBS genes of Mac7 (6,583 varaints) and Coker312 (5,173 variants). The protein-ligand and proteins-protein interaction showed a strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of cotton leaf curl disease virus. The silencing of GaNBS (OG2) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful to understand the plant adaptation mechanism.

plant biology↗

bioRxiv2020

Influenza C and D viral load in cattle correlates with bovine respiratory disease (BRD): Emerging role of orthomyxoviruses in the pathogenesis of BRD

Bovine respiratory disease (BRD) is the costliest disease affecting the cattle industry globally. Despite decades of research, the pathophysiology of BRD is not yet fully understood. It is widely believed that viruses predispose cattle to bacterial infection by causing direct damage to the respiratory tract and interfering with the immune system, leading to bacterial pneumonia. BRD remains a major challenge despite extensive vaccination against all major viral pathogens associated with the disease. Orthomyxoviruses (Influenza C & D viruses), have recently been found to infect cattle throughout the United States and are implicated to play a role in BRD. Here, we use the largest cohort study to date to investigate the association of influenza viruses in cattle with BRD. Cattle (n=599) from 3 locations were individually observed and scored for respiratory symptoms using the McGuirk scoring system. Deep pharyngeal and mid-nasal swabs were collected from each animal and were tested quantitatively for bovine viral diarrhea virus, bovine herpesvirus 1, bovine respiratory syncytial virus, bovine coronavirus, influenza C virus (ICV) and influenza D virus (IDV) by real-time PCR. Cattle that have higher viral loads of IDV and ICV also have greater numbers of co-infecting viruses than controls. More strikingly, in BRD-symptomatic cattle, the geometric mean of detectable IDV viral RNA was nearly 2 logs higher in co-infected animals (1.30x104) than those singly infected with IDV (2.19x102). This is strong evidence that viral coinfections can lead to higher replication of IDV. Our results strongly suggest that orthomyxoviruses may be significant contributors to BRD.

microbiology↗

bioRxiv2018

Germline genomic landscapes of breast cancer patients significantly predict clinical outcomes

Germline genetic variants such as BRCA1/2 play an important role in tumorigenesis and clinical outcomes of cancer patients. However, only a small fraction (i.e., 5-10%) of inherited variants has been associated with clinical outcomes (e.g., BRCA1/2, APC, TP53, PTEN and so on). The challenge remains in using these inherited germline variants to predict clinical outcomes of cancer patient population. In an attempt to solve this issue, we applied our recently developed algorithm, eTumorMetastasis, which constructs predictive models, on exome sequencing data to ER+ breast (n=755) cancer patients. Gene signatures derived from the genes containing functionally germline genetic variants significantly distinguished recurred and non-recurred patients in two ER+ breast cancer independent cohorts (n=200 and 295, P=1.4x10-3). Furthermore, we found that recurred patients possessed a higher rate of germline genetic variants. In addition, the inherited germline variants from these gene signatures were predominately enriched in T cell function, antigen presentation and cytokine interactions, likely impairing the adaptive and innate immune response thus favoring a pro-tumorigenic environment. Hence, germline genomic information could be used for developing non-invasive genomic tests for predicting patients outcomes (or drug response) in breast cancer, other cancer types and even other complex diseases.

cancer biology↗

bioRxiv2018

eTumorMetastasis, a network-based algorithm predicts clinical outcomes using whole-exome sequencing data of cancer patients

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here we developed a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles, and identify network operational gene signatures (NOG signatures) which model the tipping point at which a tumor cell shifts from a state that doesnt favor recurrences to one that does. We showed that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the most recent common ancestor of the cells within a tumor) significantly distinguished recurred and non-recurred breast tumors. These results imply that somatic mutations of tumor founders are association with tumor recurrence and can be used to predict clinical outcomes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases.

bioinformatics↗