Biology subjects

Auslander, N.

Publications and source records attributed to Auslander, N..

17 recordsLinked to original sources

bioRxiv2026

Senescence-associated loss of intestinal α1,2-fucose disrupts a modifiable host-microbiome homeostasis axis in people with HIV

BackgroundPeople with HIV (PWH), despite effective antiretroviral therapy (ART), experience disrupted intestinal homeostasis characterized by microbial dysbiosis and impaired intestinal barrier integrity, which contribute to chronic inflammation and aging-associated comorbidities. However, tractable mechanisms contributing to this dysfunction remain poorly defined. ObjectiveTo determine whether acquired loss of intestinal 1,2-fucose, a host-derived intrinsic prebiotic glycan that supports colonization by short-chain fatty acid (SCFA)-producing bacteria essential for intestinal barrier integrity, contributes to microbiome disruption, impaired epithelial resilience, inflammation, and biological aging in PWH. DesignIleal and colonic biopsies, isolated crypts, stool, and blood from PWH on ART and controls underwent multi-omic analyses. Findings were mechanistically interrogated using stool anaerobic fermentation assays and 3D intestinal organoid models of stress-mediated epithelial disruption. ResultsIn intestinal tissues, PWH exhibited reduced 1,2-fucosylation and increased senescence-associated expression of the fucose-degrading enzyme -L-fucosidase. Lower 1,2-fucose tracked with depletion of SCFA-producing bacteria, increased inflammation, and premature biological aging. In anaerobic fermentations, stool from PWH produced fewer SCFAs than controls, whereas supplementation with the human-milk-oligosaccharide-derived 1,2-fucose donor 2'-fucosyllactose restored SCFA production and improved intestinal organoid resilience to stress-mediated disruption. ConclusionThese findings identify acquired loss of intestinal 1,2-fucose as a modifiable host-microbiome mechanism linking epithelial senescence, microbial metabolic dysfunction, impaired barrier resilience, inflammation, and biological aging in treated HIV infection. SUMMARY BOXO_ST_ABSWhat is already known on this topicC_ST_ABSPeople with HIV on suppressive antiretroviral therapy frequently have persistent intestinal barrier dysfunction, microbial dysbiosis, chronic inflammation, and accelerated biological aging, but the host mechanisms that maintain this disrupted mucosal state remain incompletely defined. What this study addsThis study identifies acquired loss of intestinal 1,2-fucosylation as a feature of treated HIV infection and links this defect to a host fucosidase-high, senescence-enriched mucosal niche, depletion of SCFA-producing bacteria, impaired tight junction-associated barrier signatures, inflammation, and biological aging phenotypes. How this study might affect research, practice or policyThese findings support intestinal glycan ecology as a modifiable host-microbiome axis and provide a rationale for testing 1,2-fucose-replenishing strategies, such as 2'-fucosyllactose, to restore microbial metabolic output and improve epithelial resilience in people with HIV.

microbiology↗

bioRxiv2026

A phylogeny-guided framework for decoding mechanisms of human endogenous retrovirus regulation in health and disease

Human endogenous retroviruses (HERVs) are remmants of ancient infections which make up to [~]8% of the human genome. Their activity influences development, immunity, and cancer, but studying them has been limited by a key technical challenge: short-read sequencing cannot uniquely assign reads to these highly repetitive elements. Here, we present ERVmancer, a phylogeny-informed method that resolves the read-mapping ambiguity and quantifies HERV expression across scales, from individual loci to entire retroviral clades, depending on mapping confidence. Benchmarking with sample-matched long- and short-read data generated in this study demonsrates that ERVmancer outperforms existing approaches in both sensitivity and specificity. Application of ERVmancer recapitulates known HERV expression patterns in multiple sclerosis and uncovers new biology in breast cancer, including suppression of HERVH-LTR7 by p53. By enabling accurate and scalable quantification of integrated retroviral elements, ERVmancer provides a broadly applicable resource for investigating retroviral mechanisms in health and disease.

bioinformatics↗

bioRxiv2026

Single-cell lineage tracing maps clonal and transcriptional dynamics in melanoma metastasis

Melanoma metastasis is driven by extensive intratumoral heterogeneity and phenotypic plasticity, yet how clonal identity relates to transcriptional programs during metastasis remains unclear. Here, we applied MeRLin, a single-cell lineage tracing platform, to dissect the clonal and transcriptional heterogeneity of metastatic melanoma in a patient-derived spontaneous metastasis model. Clonal analyses revealed hierarchical structures during tumor progression, with a subset of lineages from primary tumors consistently enriched across metastatic sites, supporting a model of polyclonal seeding followed by selective expansion of pre-existing highly metastatic subpopulations. Single-cell transcriptomic profiling identified two major metastatic subpopulations of distinct transcriptional programs, characterized by neural crest stem cell-like and lipid metabolism signatures. Both programs were enriched for invasion-associated genes and maintained across organs through distinct regulatory networks. Spatial mapping by barcode RNA-FISH linked these transcriptional states to their tissue context and showed that OLFML3 expression partially co-localized with a dominant subpopulation at the tumor-liver interface, marking the invasive fronts of metastatic growth. Together, these findings establish a framework in which clonal identity, transcriptional state, and spatial organization jointly shape metastatic melanoma progression.

cancer biology↗

bioRxiv2026

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology

Somatic mutation profiling is central to cancer diagnosis and treatment selection. However, most studies focus on individual actionable mutations, overlooking the broader mutational context that shapes tumor evolution and treatment response. Here, we introduce OncoBERT, a language model that learns contextual representations of somatic mutations from large-scale clinical sequencing data spanning >210,000 patients, 113 cancer types and 20 institutions. OncoBERT uncovers robust patient-specific mutational subtypes across diverse cohorts and targeted sequencing panels, revealing clinically meaningful mutation patterns that are associated with differential response to chemotherapy, targeted therapies, and immunotherapy. Importantly, integrating OncoBERTs contextual representations with clinically approved biomarkers of immunotherapy response, such as tumor mutational burden (TMB) and microsatellite instability (MSI), significantly improved prediction of clinical benefit. By further incorporating matched tumor transcriptomic profiles, we linked OncoBERT-defined mutational subtypes to distinct cancer hallmark programs and tumor microenvironment states. Together, OncoBERT provides a scalable framework for deciphering somatic mutational landscapes, enabling improved patient stratification and advancing precision oncology.

genomics↗

bioRxiv2025

A K27-linked Ubiquitin Checkpoint Controls NOTCH Homeostasis

Cell surface receptors such as NOTCH1 must be tightly regulated to ensure developmental fidelity and prevent pathological activation. Although the proteolytic steps culminating in nuclear NOTCH1 signaling are established, how cells prevent excessive or uncontrolled activation has remained unresolved. Here we identify the autophagy-related protein UVRAG as a negative regulator of NOTCH1. Upon receptor activation, UVRAG, acting independently of autophagy, recruits and activates the E3-ligase ITCH to catalyze K27-linked ubiquitination of membrane-tethered NOTCH1, thereby licensing ESCRT-dependent lysosomal degradation. Disruption of the UVRAG-ITCH-ESCRT axis stabilizes activated NOTCH1 intermediates and amplifies oncogenic signaling. In T-cell leukemia models driven by constitutive NOTCH1 activity, restoring UVRAG expression reinstates receptor turnover, suppresses disease progression, and improves therapeutic response. These findings define a ubiquitin-directed safeguard circuit that enforces NOTCH1 signaling homeostasis and reveals a tunable axis for intervention in NOTCH1-driven cancers.

molecular biology↗

bioRxiv2025

Taxonomy-free fecal microbiome profiles enable robust prediction of immunotherapy response and toxicity in melanoma

The gut microbiome has been causally linked to the efficacy of immune-checkpoint inhibitor therapy (ICI), prompting numerous clinical trials of microbiome-targeting strategies. Yet, mechanisms by which gut microbiota shape immune responses remain elusive as taxonomic biomarkers have failed to generalize across multiple cohorts. In this study, we develop a taxonomy-agnostic framework to identify microbial biomarkers of ICI response and immune-related adverse event (irAE) occurrence from metagenomic sequencing. Applying this approach to four independent melanoma cohorts from clinical centers across the United States, we uncover gut microbial proteins produced by diverse bacterial taxa that consistently predict ICI response. Notably, we uncover a previously uncharacterized operon involved in cellular redox homeostasis that is encoded by different bacteria and reliably predicts irAE occurrence. We further validated the predictive power of this operon in a prospectively sequenced melanoma cohort. Our results demonstrate that taxa-agnostic microbial protein biomarkers are robust, generalizable, and provide a path towards pretreatment risk stratification for melanoma patients initiating ICI therapy.

cancer biology↗

bioRxiv2025

Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance

Most advanced melanomas initially respond to targeted therapy but eventually relapse. Rather than acquiring new mutations, resistance is driven by drug-tolerant persister cells that enter a reversible drug-refractory state. We developed MeRLin, a high-resolution lineage tracing platform integrating cellular barcoding, single-cell transcriptomics, RNA fluorescence in situ hybridization (FISH), and computational analyses to track clonal and transcriptional dynamics in patient-derived melanoma models during prolonged therapy. Clonal dynamics revealed that persister subpopulations first responded to treatment but persisted and expanded during minimal residual disease, ultimately leading to tumor recurrence. Pre-treatment melanoma populations diversified into four conserved persister states characterized by stress-like, lipid metabolism, PI3K signaling, and extracellular matrix remodeling programs associated with adaptive resistance. Spatial transcriptomics showed the organization of these adaptive programs and a complex signaling network of autocrine and paracrine interactions among persister subpopulations. Barcoded RNA-FISH enabled spatial mapping of clonal identity and gene expression, revealing in situ co-localization of a dominant resistant clone with SLC2A1 expression. MeRLin provides a robust framework for dissecting cancer heterogeneity and identifying vulnerabilities in persister populations.

cancer biology↗

bioRxiv2024

A selective S-acyltransferase inhibitor suppresses tumor growth

S-acyltransferases play integral roles in essential physiological processes including regulation of oncogenic signaling pathways. While discovered over 40 years ago the field still lacks specific S-acylation inhibitors thus the potential benefit of pharmacologically targeting S-acyltransferases for human disease is still unknown. Here we report the identification of an orally bioavailable acyltransferase inhibitor SD-066-4 that inhibits the acyltransferase ZDHHC20. We identified a specific alanine residue that accommodates the methyl group of SD-066-4, thus providing isoform selectivity. SD-066-4 stably reduces EGFR S-acylation in Kras mutant cells and blocks the growth of Kras mutant lung tumors extending overall survival. We find that lung cancer patients harboring deletions in ZDHHC20 or ZDHHC14 concurrent with Kras alterations have a significant survival benefit, underscoring the translational importance of these enzymes.

cancer biology↗

bioRxiv2024

Targeting LxCxE cleft pocket of retinoblastoma protein in M2 macrophages inhibits ovarian cancer progression

AbstractOvarian cancer remains a major health threat with limited treatment options available. It is characterized by immunosuppressive tumor microenvironment (TME) maintained by tumor- associated macrophages (TAMs) hindering anti-tumor responses and immunotherapy efficacy. Here we show that targeting retinoblastoma protein (Rb) by disruption of its LxCxE cleft pocket, causes cell death in TAMs by induction of ER stress, p53 and mitochondria-related cell death pathways. A reduction of pro-tumor Rbhigh M2-type macrophages from TME in vivo enhanced T cell infiltration and inhibited cancer progression. We demonstrate an increased Rb expression in TAMs in women with ovarian cancer is associated with poorer prognosis. Ex vivo, we show analogous cell death induction by therapeutic Rb targeting in TAMs in post-surgery ascites from ovarian cancer patients. Overall, our data elucidates therapeutic targeting of the Rb LxCxE cleft pocket as a novel promising approach for ovarian cancer treatment through depletion of TAMs and re-shaping TME immune landscape. Statement of significanceCurrently, targeting immunosuppressive myeloid cells in ovarian cancer microenvironment is the first priority need to enable successful immunotherapy, but no effective solutions are clinically available. We show that targeting LxCxE cleft pocket of Retinoblastoma protein unexpectedly induces preferential cell death in M2 tumor-associated macrophages. Depletion of immunosuppressive M2 tumor-associated macrophages reshapes tumor microenvironment, enhances anti-tumor T cell responses, and inhibits ovarian cancer. Thus, we identify a novel paradoxical function of Retinoblastoma protein in regulating macrophage viability as well as a promising target to enhance immunotherapy efficacy in ovarian cancer.

immunology↗

bioRxiv2023

kMermaid: Ultrafast functional classification of microbial reads

Shotgun metagenomic sequencing can determine both taxonomic and functional content of microbiomes. However, current functional classification methods for metagenomic reads require substantial computational resources and yield ambiguous classifications, limiting downstream quantitative analyses. Existing k-mer based methods to classify microbial sequences into species-level groups have immensely improved taxonomic classification, but this concept has not been extended to functional classification. Here we introduce kMermaid, for classifying metagenomic reads into functional clusters of proteins. Using protein k-mers, kMermaid allows for highly accurate and ultrafast functional classification, with a fixed memory usage, and can easily be employed on a typical computer.

microbiology↗

bioRxiv2022

Characterizing the landscape of viral expression in cancer by deep learning

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

bioinformatics↗

bioRxiv2020

Prediction of the virus incubation period for COVID-19 and future outbreaks

A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. Here, we explore different genomic features of RNA viruses that correlate with the incubation times and provide a predictive model that accurately estimates the upper limit incubation time for diverse viruses including SARS-CoV-2, and thus, could help control future outbreaks.

microbiology↗

bioRxiv2020

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

SARS-CoV-2 poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and MERS-CoV, from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial elements of coronavirus pathogenicity and possible targets for diagnostics, prognostication and interventions.

microbiology↗

bioRxiv2020

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

Advances in metagenomics enable massive discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. The existing methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct bacteriophage families. We present Seeker, a deep-learning tool for reference-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and clean differentiation of phage sequences from bacterial ones, even for phages with little sequence similarity to established phage families. We comprehensively validate Seekers ability to identify unknown phages and employ Seeker to detect unknown phages, some of which are highly divergent from known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly python package (https://github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

microbiology↗

bioRxiv2019

Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer

BackgroundChronic inflammation and chromosome aneuploidy are major traits of primary liver cancer (PLC), which represent the second most common cause of cancer related death worldwide. Increased cancer fitness and aggressiveness of PLC may be achieved by enhancing tumoral genomic complexity that alters tumor biology.\n\nMethodHere, we developed a scoring method, namely functional genomic complexity (FGC), to determine the degree of molecular heterogeneity among 580 liver tumors with diverse ethnicities and etiologies by assessing integrated genomic and transcriptomic data.\n\nResultsWe found that tumors with higher FGC scores are associated with chromosome instability and TP53 mutations, and a worse prognosis, while tumors with lower FGC scores have elevated infiltrating lymphocytes and a better prognosis. These results indicate that FGC scores may serve as a surrogate to define genomic heterogeneity of PLC linked to chromosomal instability and evasion of immune surveillance.\n\nConclusionOur findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy.\n\nSTATEMENT OF SIGNIFICANCEGenomic heterogeneity contributes to therapeutic failure and poor outcome in patients with liver cancer and poses a challenge in defining targeted therapy. Our findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy. \\body

genomics↗

bioRxiv2019

In silico learning of tumor evolution through mutational time series

Cancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ Long Short-Term Memory networks (LSTMs), a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capacity of LSTMs to learn complex dynamics of the mutational time series governing tumor progression, allowing accurate prediction of the mutational burden and the occurrence of mutations in the sequence. Using the probabilities learned by the LSTM, we simulate mutational data and show that the simulation results are statistically indistinguishable from the empirical data. We identify passenger mutations that are significantly associated with established cancer drivers in the sequence and demonstrate that the genes carrying these mutations are substantially enriched in interactions with the corresponding driver genes. Breaking the network into modules consisting of driver genes and their interactors, we show that these interactions are associated with poor patient prognosis, thus likely conferring growth advantage for tumor progression. Thus, application of LSTM provides for prediction of numerous additional conditional drivers and to reveal hitherto unknown aspects of cancer evolution.\n\nSignificanceCancer is caused by the effects of somatic mutations known as drivers. Although a number of major cancer drivers have been identified, it is suspected that many more comparatively rare and conditional drivers exist, and the interactions between different cancer-associated mutations that might be relevant for tumor progression are not well understood. We applied an advanced neural network approach to learn the sequence of mutations and the mutational burden in colon and lung cancers, and to identify mutations that are associated with individual drivers. A significant ordering of driver mutations is demonstrated, and numerous, previously undetected conditional drivers are identified. These findings broaden the existing understanding of the mechanisms of tumor progression and have implications for therapeutic strategies.

bioinformatics↗

bioRxiv2019

Cancer-type specific aneuploidies hard-wire chromosome-wide gene expression patterns of their tissue of origin

Most carcinomas have characteristic chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is unknown. As aneuploidies directly affect gene expression, we hypothesized that cancer-type specific aneuploidies, which emerge at early stages of tumor evolution, confer adaptive advantages to the physiological requirements of the tissue of origin. To test this hypothesis, we compared chromosomal aneuploidies reported in the TCGA database to chromosome arm-wide gene expression levels of normal tissues from the GTEx database. We find that cancer-type specific chromosomal aneuploidies mirror differential gene expression levels specific to the respective normal tissues which cannot be explained by copy number alterations of resident cancer driver genes. We propose that cancer-type specific aneuploidies "hard-wire" chromosome arm-wide gene expression levels present in normal tissues, favoring clonal expansion and tumorigenesis. One sentence summaryThe clonal evolution of cancer is initiated by tissue-specific transcriptional requirements

cancer biology↗