Biology subjects

Baig, Z.

Publications and source records attributed to Baig, Z..

6 recordsLinked to original sources

bioRxiv2026

B-cell SIGLEC-5 engages T-cell components of the elastin receptor complex (ERC) to suppress inflammatory T-cell cytokines

SIGLECs remain poorly defined in human B-cell biology beyond SIGLEC-2/CD22 and SIGLEC-10. Here, we identify a previously unrecognized regulatory pathway involving the paired receptors SIGLEC-5 and SIGLEC-14 at the human B-T-cell interface. We show that activated B-cells differentially regulate these receptors: SIGLEC-5 is predominantly surface-expressed and induced by CD40 engagement, whereas SIGLEC-14 is primarily secreted and upregulated after both CD40 and TLR9 stimulation. We further identify EBP (elastin binding protein) and CTSA (cathepsin A) components of the elastin receptor complex (ERC), expressed by activated T-cells, as a novel ligand for both SIGLEC-5 and SIGLEC-14. Functionally, ERC-associated engagement of SIGLEC-5 on B-cells suppresses T-cell IFN-{gamma} and IL-17 expression, establishing SIGLEC-5 as a B-cell-expressed inhibitory SIGLEC that restrains inflammatory T-cell cytokine responses. SIGLEC-14 does not alter this suppression, as SIGLEC-5{square} B-cells from SIGLEC-14-sufficient and -null individuals show comparable inhibitory activity. These findings broaden SIGLEC-mediated adaptive immune regulation, with relevance to inflammatory and autoimmune disease.

immunology↗

bioRxiv2025

Machine Learning-Augmented Analysis of Nano-electrochemical Sensor Data for Predictive and Quantitative Assays of Complex Biological Samples

Analytical chemistry provides the content of nearly every scientific, technical and business decision relating to what atoms, molecules and devices are and how they interact in their environment. Various workflows come together in describing those interactions--separation, identification, quantitation, classification--to inform decisions across society. Each workflow has associated equipment, infrastructure and subject matter experts that integrate results for input into those decisions. However, those existing workflows are constrained by their evolution and often require integrated applications of the multiple analytical techniques integrated to form conclusions, thereby increasing cost, complexity and quality. This paper describes an alternative approach to addressing the many limitations of contemporary analytical chemistry. Here machine learning is leveraged to analyze high dimensional data derived from a nano-electrochemical sensor platform, to perform classification, identification and quantitation analyses in an integrative manner from a single-shot measurement. The approach described here for biological samples does not require sample-specific preparation or marker-specific probes and labels to identify and/or quantitate the analyte of interest, thereby liberating the physical measurement of the sample from chemicals and workflows that are tied to the underlying biochemical hypothesis. The nano-electrochemical sensor, as described in a prior publication, transduces the vibrational frequencies of multiple species in the sample into a collection of discrete electronic signatures that represent the sample in a high dimensional space, while using only 2-4{micro}l sample volume. The sample analysis relies on training data to identify features in the high dimensional input dataset that are unique to the molecule or phenotype under consideration and distinct from the sample background matrix and/or a set of defined sample controls. The molecule- or phenotype-associated features can be tracked with clinically identified disease burden in the patient or with biomolecule concentration in the biological sample. The identification of patients with dementia from a plasma test is outlined here to demonstrate the applicability of this approach to classifying phenotypes associated with complex neurological disorders in a marker-agnostic manner. This approach is applied to a specific and quantitative assay of insulin Humalog and insulin Toujeo in a batch that comprises of a mixture of the two peptides, where Toujeo differs from Humalog in three amino acids residues only. We also describe the development of an assay for IL-6 in spiked human plasma samples with this method, thereby demonstrating the applicability of this analysis paradigm to more complex samples and protein-like species.

bioengineering↗

bioRxiv2025

Tumor-infiltrating CD27-IgD- regulatory B cells suppress cytotoxic CD8+T cell responses in renal cell carcinoma

B cells play a pivotal role in shaping the tumor microenvironment (TME) and tertiary lymphoid structures (TLS), but the functions of specific B cell subsets in cancer pathogenesis remain unclear. Using a novel tissue-centric single cell RNA-sequencing (scRNA-seq) bioinformatic workflow aimed at unraveling cancer-specific clusters, combined with flow cytometry and high-plex spatial imaging, we identify a renal cell carcinoma (RCC)-specific enrichment of CD27-IgD-CD21+CD11c- double negative 1 (DN1) B cells associated with worse prognosis and regulatory function. Spatial profiling localizes DN1 Bregs within immature TLSs, in close proximity to IL-10 and TGF{beta}CD8T cells. RCC-resident DN1 B cells are enriched for endosomal Toll-like receptor (TLR) signaling pathways and stimulation of tumor-infiltrating lymphocytes (TILs) with TLR agonists induces the differentiation of IL-10+ and TGF{beta}+DN Bregs suppressing CD8+T cell cytotoxicity, partially via IL-10 and TGF{beta}. These findings identify a pro-tumorigenic B cell population with potential diagnostic and therapeutic relevance in RCC.

immunology↗

bioRxiv2025

Multi-step antibody class switching in a primary human response is restricted after IGHG2 and dependent on B cell maturation stage.

Class switch recombination (CSR) allows the formation of functionally specialized antibodies. Understanding of CSR dynamics is key for better design and prediction of vaccines to protect mucosal surfaces. To investigate CSR in a primary human immune response under a controlled setting, we sampled healthy volunteers without COVID-19 history every other day during the first three weeks after SARS-CoV-2 vaccination, with additional time points up to six months. Leveraging bulk and single-cell B cell receptor repertoires, single-cell transcriptomics, and immunophenotyping, we uncover paradigm-shifting insights into CSR. Newly activated B cells produce sterile transcripts of all antibody isotype constant regions (IGHC) simultaneously up to IGHG2, challenging the view that sterile transcription occurs for only a single IGHC gene at a time. CSR follows a multistep progression along the IGHC locus; in this challenge vaccine-induced B cells switch from IGHM to IGHG3 and IGHG1, followed by a subsequent switching to IGHA1 and IGHG2 after secondary immunization. IGHA2 clones require pre-switching to IGHA1 clones. Notably, switching tendency, measured by IGHC sterile transcription, is memory B cell subtype dependent, particularly beyond IGHG2. Contrary to other vaccines, antigen-specific B cells are enriched in DN2, Cmem2 and DN4 subtypes after immunization. We also observe a temporal decoupling of CSR and somatic hypermutation (SHM), with the latter detectable only after six months post-immunization. Our data describes the dynamics between CSR, SHM, sterile transcription and B cell memory development during a human primary response, challenges textbook models of CSR and offers new insights to aid control of CSR direction. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/652638v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@785769org.highwire.dtl.DTLVardef@b2c54eorg.highwire.dtl.DTLVardef@b78c72org.highwire.dtl.DTLVardef@65b5aa_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryClass switch recombination occurs independently of somatic hypermutation and in a multistep fashion up to IGHG2 during a primary response in humans.

immunology↗

bioRxiv2021

Host bioenergetic parameters reveal cytotoxicity of anti-tuberculosis drugs undetected using conventional viability assays.

High attrition rates in tuberculosis (TB) drug development have been largely attributed to safety, which is likely due to the use of endpoint assays measuring cell viability to detect drug cytotoxicity. In drug development of cancer, metabolic and neurological disorders, and antibiotics, cytotoxicity is increasingly being assessed using extracellular flux (XF) analysis, which measures cellular bioenergetic metabolism in real-time. Here, we adopt the XF platform to investigate the cytotoxicity of drugs currently used in TB treatment on the bioenergetic metabolism of HepG2 cells, THP-1 macrophages, and human monocyte derived macrophages (hMDM). We found that the XF analysis reveals earlier drug-induced effects on the cells bioenergetic metabolism prior to cell death, measured by conventional viability assays. Furthermore, each cell type has a distinct response to drug treatment, suggesting that more than one cell type should be considered to examine cytotoxicity in TB drug development. Interestingly, chemically unrelated drugs with different modes of action on Mycobacterium tuberculosis have similar effects on the bioenergetic parameters of the cells, thus, discouraging the prediction of potential cytotoxicity based on chemical structure and mode of action of new chemical entities. The clustering of the drug-induced effects on the hMDM bioenergetic parameters are reflected in the clustering of the effects of the drugs on cytokine production in hMDMs, demonstrating concurrence between the effects of the drugs on the metabolism and functioning of the macrophages. These findings can be used as a benchmark to establish XF analysis as a new tool to assay cytotoxicity in TB drug development.

pharmacology and toxicology↗

bioRxiv2019

Low membrane fluidity triggers lipid phase separation and protein segregation in vivo

All living organisms adapt their membrane lipid composition in response to changes in their environment or diet. These conserved membrane-adaptive processes have been studied extensively. However, key concepts of membrane biology linked to regulation of lipid composition including homeoviscous adaptation maintaining stable levels of membrane fluidity, and gel-fluid phase separation resulting in domain formation, heavily rely upon in vitro studies with model membranes or lipid extracts. Using the bacterial model organisms Escherichia coli and Bacillus subtilis, we now show that inadequate in vivo membrane fluidity interferes with essential complex cellular processes including cytokinesis, envelope expansion, chromosome replication/segregation and maintenance of membrane potential. Furthermore, we demonstrate that very low membrane fluidity is indeed capable of triggering large-scale lipid phase separation and protein segregation in intact, protein-crowded membranes of living cells; a process that coincides with the minimal level of fluidity capable of supporting growth. Importantly, the in vivo lipid phase separation is not associated with a breakdown of the membrane diffusion barrier function, thus explaining why the phase-separation process induced by low fluidity is biologically reversible.

microbiology↗