Biology subjects

Nixon, A.

Publications and source records attributed to Nixon, A..

8 recordsLinked to original sources

bioRxiv2025

Advancing Codon Language Modeling with Synonymous Codon Constrained Masking

Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on 6 of 7 benchmarks sensitive to DNA-level features, including mRNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.

bioinformatics↗

bioRxiv2024

Marker gene fishing for single-cell data with complex heterogeneity

In single-cell studies, cells can be characterized with multiple sources of heterogeneity such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance sources of heterogeneity (SOH) are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations correponding to a SOH (e.g., cell activation status), isolating it from other sources of heterogeneity (e.g., cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis (IPF) lung tissues, new T cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns which were previously neglected. This paper highlights B-Lightnings potential as a powerful tool for single-cell data analysis, particularly in complex data sets where sources of heterogeneity of interest are entangled with numerous nuisance factors.

bioinformatics↗

bioRxiv2024

Adversarial learning enables unbiased organism-wide cross-species alignment of single-cell RNA data

Todays single-cell RNA (scRNA) datasets remain siloed, due to significant challenges associated with their integration at scale. Moreover, most scRNA analysis tools that operate at scale leverage supervised techniques that are insufficient for cell-type identification and discovery. Here, we demonstrate that the alignment of scRNA data using unsupervised models is accurate at an organism-wide scale and between species. To do this, we show adversarial training of a deep-learning model we term batch-adversarial single-cell variational inference (BA-scVI) can be employed to align standardized benchmark datasets comprising dozens of scRNA studies spanning tissues in humans and mice. In the aligned space, we analyze cell types that span tissues in both species and find prevalent complement expressing macrophages and fibroblasts. We provide access to the tools presented via an online interface for atlas exploration and reference-based drag-and-drop alignment of new data.

genetics↗

bioRxiv2022

Segmental flexibility of bispecific T-cell engagers regulates the dynamics of immune synapse formation

Bispecific T-cell engagers (TcEs) link T cell receptors to tumor-associated antigens on cancer cells, forming cytotoxic immunological synapses (IS). Close membrane-to-membrane contact ([≤]13 nm) has been proposed as a key mechanism of TcE function. To investigate this and identify potential additional mechanisms, we compared four immunoglobulin G1-based (IgG1) TcE Formats (A-D) targeting CD3{varepsilon} and Her2, designed to create varying intermembrane distances (A B=C>D. In a minimal system for IS formation on SLBs, TcE performance followed the trend A=B=C>D. Addition of close-contact requiring CD58 co-stimulation revealed phospholipase C-{gamma} activation matching cytotoxicity with A>B=C>D. Our findings suggest that, when adhesion is equivalent, TcE potency is determined by two parameters: contact distance and flexibility. Both the close/far-contact formation axis and the low/high flexibility axis significantly impact TcE potency, explaining the similar potency of Format B (close-contact/high flexibility) and C (far-contact/low flexibility). Significance statementBispecific T-cell engagers (TcEs) are immunotherapeutic drugs that trigger the destruction of cancer cells by linking T cells to cancer cell through specific surface molecules (antigens). We designed a series of TcEs with varying distances between their binding sites and flexibilities of the TcE-antigen complexes. By combining structural and functional analyses, we confirmed close-contact formation between T cells and cancer cells as a critical determinant, mediated by co-activating receptors. Furthermore, we also identified molecular flexibility of the TcE-antigen complex as a further critical parameter for TcE potency. These findings provide novel insights into TcE function and highlight the importance of both parameters for future research and the design of improved immunotherapies.

immunology↗

bioRxiv2022

Characterization of the minimal residual disease state reveals distinct evolutionary trajectories of human glioblastoma

Recurrence of solid tumors renders patients vulnerable to a distinctly advanced, highly treatment-refractory disease state that has an increased mutational burden and novel oncogenic drivers not detected at initial diagnosis. Improving outcomes for recurrent cancers requires a better understanding of cancer cell populations that expand from the post-therapy, minimal residual disease (MRD) state. We profiled barcoded tumor stem cell populations through therapy at tumor initiation/engraftment, MRD and recurrence in our therapy-adapted, patient-derived xenograft models of glioblastoma (GBM). Tumors showed distinct patterns of recurrence in which clonal populations exhibited either an a priori, pre-existing fitness advantage, or a priori equipotency fitness acquired through therapy. Characterization of the MRD state by single-cell and bulk RNA sequencing revealed a tumor-intrinsic immunomodulatory signature with strong prognostic significance at the transcriptomic level and in proteomic analysis of cerebrospinal fluid (CSF) collected from GBM patients at all stages of disease. Our results provide insight into the innate and therapy-driven dynamics of human GBM, and the prognostic value of interrogating the MRD state in solid cancers.

cancer biology↗

bioRxiv2022

Reciprocal epigenetic Sox2 regulation by SMAD1-SMAD3 is critical for anoikis resistance and metastasis in cancer

Growth factors in the tumor environment are key regulators of cell survival and metastasis. Here we reveal, dichotomy between TGF-{beta} superfamily growth factors BMP and TGF-{beta}/activin, and their downstream SMAD effectors. Gene expression profiling uncovered Sox2 as a key signaling node regulated in an opposing manner by anoikis-promoting BMP2, 4 and 9, and anoikis-suppressing TGF-{beta} and activin A. We find that Sox2 repression by BMPs robustly inhibits intraperitoneal tumor burden and increases survival in multiple ovarian cancer models. Repression of Sox2 is driven by SMAD1 dependent histone H3K27me3 recruitment and DNA methylation at SOX2s promoter. Conversely, TGF-{beta} and activin A promote Sox2 expression, and anoikis resistance by SMAD3 mediated histone H3K4me3 recruitment. We find that balancing Sox2 levels is critical for anoikis, as transcriptomics reveals regulation of key cell death pathways. Moreover, BMP-driven SMAD1 signaling can override TGF-{beta} and activins effect on Sox2, which has clinical significance due to the high levels of TGF-{beta} we find in ovarian cancer patients. Together, our findings identify Sox2 as a contextual and contrastingly regulated key node, downstream of TGF-{beta} superfamily members controlling anoikis and metastasis in ovarian cancers. HighlightsO_LISox2 is a key node for anoikis resistance in cancer C_LIO_LISox2 is differentially regulated by TGF-{beta}/activin and BMPs in broad cancers C_LIO_LIBMP9 is a robust metastasis suppressor by lowering Sox2 C_LIO_LISox2 regulation is contextual, epigenetic and at the transcriptional level C_LI

cancer biology↗

bioRxiv2020

Ontogeny and Vulnerabilities of Lapatinib Drug-Tolerant Persisters in HER2+ Breast Cancer

Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. "Drug-tolerant persisters" (DTPs), a sub-population of cancer cells that survive via reversible, non-genetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKIs) in other malignancies, but DTPs following HER2 TKI exposure have not been well characterized. We found that HER2 TKIs evoke DTPs with a luminal-like or a mesenchymal-like transcriptome. Lentiviral barcoding/single cell RNA-sequencing reveal that HER2+ breast cancer cells cycle stochastically through a "pre-DTP" state, characterized by a G0-like expression signature and enriched for diapause and/or senescence genes. Trajectory analysis/cell sorting show that pre-DTPs preferentially yield DTPs upon HER2 TKI exposure. Cells with similar transcriptomes are present in HER2+ breast tumors and are associated with poor TKI response. Finally, biochemical experiments indicate that luminal-like DTPs survive via estrogen receptor-dependent induction of SGK3,leading to rewiring of the PI3K/AKT/mTORC1 pathway to enable AKT-independent mTORC1 activation. STATEMENT OF SIGNIFICANCEDTPs are implicated in resistance to TKIs, other targeted therapies, and chemotherapy, but their ontogeny and vulnerabilities remain unclear. We find that HER2 TKI-DTPs emerge from stochastically arising primed cells ("pre-DTPs") that preferentially engage either of two distinct transcriptional programs upon TKI exposure. Our results provide new insights into DTP ontogeny and identify potential therapeutic vulnerabilities.

cancer biology↗

bioRxiv2020

A Precision Medicine Approach to Stress Testing Using Metabolomics and MicroRibonucleic Acids

BackgroundAcute coronary syndrome (ACS) is a growing global health problem, and precision medicine techniques hold promise for the development of diagnostic indicators of ACS. In this pilot, we sought to assess the utility of an integrated analysis of metabolomic and microRNA data in peripheral blood to distinguish patients with abnormal cardiac stress testing from matched controls. MethodsWe used prospectively collected samples from emergency department (ED) patients placed in an ED-based observation unit who underwent stress testing for ACS. We isolated microRNA and quantified metabolites from plasma collected before and after stress testing in patients with myocardial ischemia on stress testing versus those with normal stress tests. The combined metabolomic and microRNA data were analyzed jointly for case (ischemia) and 1:1 matched control patients in a supervised, dimension-reducing discriminant analysis. Two integrative models were implemented: a baseline model utilizing data collected prior to stress-testing (T0) and a stress-delta model, which included the difference between post-stress test (T1) and pre-stress test (T0). ResultsSeven case patients with myocardial ischemia on ED cardiac stress testing (6 females, 85% Caucasian, mean Thrombolysis In Myocardial Infarction Score=3, 4 patients ultimately received percutaneous coronary intervention) were 1:1 age and sex-matched to controls. Several metabolites and microRNAs were differentially expressed between cases and controls. Integrative analysis of the baseline levels of metabolites and microRNA expression showed modest performance for distinguishing cases from controls with an overall error rate of 0.143. The stress-delta model showed worse performance for distinguishing cases from controls, with an overall error rate of 0.500. ConclusionsGiven our small sample size, results are hypothesis-generating. However, this pilot study shows a potential method for a precision medicine approach to cardiac stress testing in patients undergoing workup for ACS.

physiology↗