Biology subjects

Nawaz, Z.

Publications and source records attributed to Nawaz, Z..

3 recordsLinked to original sources

bioRxiv2024

A regulatory B cell subpopulation expressing CD301b lectin promotes breast cancer growth

Aberrant tumor glycosylation can alter immune recognition; however, the specific influence of glycan-lectin interactions on tumor progression remains poorly understood. Here, we identify the C-type lectin receptor CD301b (encoded by Mgl2) as a regulator of immune activity within the breast tumor microenvironment (TME). Using a murine triple-negative breast cancer model, we demonstrate that tumors expressing the Tn glycoantigen grow more rapidly, and this growth is facilitated by CD301b immune cells. Depletion or genetic loss of CD301b markedly suppressed tumor growth, indicating that CD301b promotes tumor progression through myeloid-tumor interactions. Phenotypic analyses revealed that CD301b cells within tumors are type 2 conventional dendritic cells (cDC2s), a subset known to influence immune polarization. Single-cell RNA sequencing of human breast cancers showed that the human ortholog CLEC10A is expressed in cDC2-like dendritic cells and select macrophage subsets, suggesting a conserved role for CD301 myeloid populations. Transcriptomic profiling of tumors developed in Mgl2-deficient mice revealed a shift toward an inflammatory, immune-activated state consistent with enhanced antitumor immunity. Together, these findings establish a link between tumor glycosylation and lectin signaling of myeloid cells, highlighting CD301b as a potential target for reprogramming the tumor immune microenvironment in breast cancer.

immunology↗

bioRxiv2021

Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis

MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation. ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets. AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2

bioinformatics↗

bioRxiv2019

Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is timeconsuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder). Author SummaryLinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI de-velopments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.

bioinformatics↗