Biology subjects

Shen-Orr, S. S.

Publications and source records attributed to Shen-Orr, S. S..

9 recordsLinked to original sources

bioRxiv2026

Immune aging captures complementary aging biology beyond epigenetic clocks

Biological aging clocks are typically evaluated through competitive benchmarking, implicitly assuming that a single metric can sufficiently capture the complexities of aging1-6. Here, we tested an alternative hypothesis: that distinct clock types capture orthogonal dimensions of aging and therefore yield greater value when integrated. Using the Framingham Heart Study, we compared the immune-aging metric, IMM-AGE, with established DNA methylation clocks and found that integrated models consistently outperformed single-clock approaches. To investigate the basis of this complementarity, we derived IMMAGE-Epi, a 22-CpG methylation surrogate of IMM-AGE which exhibited minimal overlap with canonical epigenetic clock CpGs, suggesting that immune aging is associated with a distinct methylomic feature and pathway space rather than representing a reformulation of existing clock architectures. Together, our findings support an emerging multidimensional model of biological aging in which integrating orthogonal biological clocks may offer greater translational utility than competitive single-clock optimization.

immunology↗

bioRxiv2026

Immune Aging is an Independent Risk Factor for Cardiovascular Disease

Cardiovascular disease remains the leading cause of mortality, yet current clinical predictors miss substantial disease-risk. While the immune system contributes to this residual risk, its complexity has hindered broadly applicable, clinically scalable metrics of immune-state. Here, we establish the prognostic relevance of IMM-AGE, a system-level metric of immune-aging, to cardiovascular disease. We learn reference-free, low-dimensional representations of IMM-AGE across cell, protein, and mRNA measurements, enabling high-fidelity quantification across modalities, blood fractions, and platforms, including standard hospital flow cytometers. Among UK-Biobank participants, 56.9% of IMM-AGE variation remained unexplained by routine clinical measures, and across diverse cohorts totaling ~48,000 individuals, elevated IMM-AGE was independently associated with future cardiovascular risk, intervention outcomes, and mortality. Moreover, incorporation of IMM-AGE into the PREVENT 10-year risk equation significantly improved risk stratification. These findings establish immune-aging as an independent biological dimension of cardiovascular disease-risk and support IMM-AGE as a practical tool for precision risk assessment.

immunology↗

bioRxiv2025

Bacterial exposure drives immune ageing

The immune system is uniquely capable of both regenerating and ageing the rest of the organism based on its own state1. Understanding the driving forces behind alterations in the immune system with age2 has thus potential to lead to interventions against ageing in general3. However, the factors driving immune ageing remain largely unknown4. Here we show that alterations in response to bacterial exposure are a key driving force of immune ageing. Studying a longitudinal cohort with their immune system characterised in high-dimension2, we devised a phenotype-driven gene co-regulation analysis through which we unravel the immune-ageing role for genes regulated by miRNAs previously reported to cause gut leakiness5. Hypothesizing increased bacterial exposure as a driver of immune ageing, we estimated the exposure of each individuals immune system to bacteria through analysing the residual bacterial DNA content in their immune cells6. We found that the total bacterial load is unrelated to the subjects chronological or immune age, but predicts the magnitude of change in immune age over the next year. Finally, we rationalise previously observed alterations in cellular composition of the immune system associated with ageing2 by finding that the immune cell subtypes most increasing in their frequency are highly enriched in expressing bacterial response genes. Our results thus earmark bacterial exposure as the candidate driver to be counteracted in anti-ageing interventions and the immune cell residual bacterial DNA content as a biomarker for the immune systems most at risk of ageing. Overall, these results offer a new paradigm for investigating gut health in the context of immune ageing7,8.

immunology↗

bioRxiv2025

Systems level analysis of B-cell development identifies BDNF as a driver for human B lymphopoiesis

B-cell aplasia is a major consequence of aging, chemotherapy, and B-cell-depleting immunotherapies, compromising immune protection against infections, cancer, and vaccines. Yet, unlike the myeloid and erythroid lineages, no strategy exists to accelerate human B-cell reconstitution. Here, we used an integrative systems biology approach to identify regulators of human B lymphopoiesis in the bone marrow (BM) microenvironment. By combining single-cell transcriptomic analysis of human BM with intercellular communication mapping, we generated an initial set of candidate factors predicted to act on developing B cells. To distinguish biologically meaningful putative regulators from a broad candidate space, we further intersected these findings with orthogonal human datasets capturing age-impaired B lymphopoiesis and protein dynamics associated with B-cell depletion and reconstitution. This convergent prioritization strategy highlighted a focused set of putative regulators, among which brain-derived neurotrophic factor (BDNF) emerged repeatedly as a top putative regulator. Functional interrogation in progenitor BM cells showed that several prioritized putative regulators induced transcriptional programs linked to early immune development, with BDNF consistently promoting pathways associated with B-cell differentiation. Importantly, in a human in vitro BM co-culture system, BDNF enhanced the differentiation of CD34+ hematopoietic progenitors into CD19+ progenitor B cells. Together, these findings identify BDNF as a previously unrecognized regulator of early human B lymphopoiesis and establish a general framework for uncovering functional hematopoietic regulators by integrating single-cell analysis with complementary biological and clinical signals. This approach may support future strategies to improve immune reconstitution in settings of prolonged B-cell depletion.

systems biology↗

bioRxiv2021

Multiple trajectory alignment reconstructs disease dynamics for discovery and clinical benefit

Diseases change over time, both phenotypically and in underlying driving molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult, due to their complexity and the high heterogeneity between individuals. We developed TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional short time-series data. We demonstrate TimeAx utility by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identified a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiated between early and late tumors within the same tumor subtype, uncovering novel molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics, providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

systems biology↗

bioRxiv2021

CytoPro: A computational platform for accurate and robust assessment of cell contributions in bulk expression from diverse tissues and conditions

Cells are the quanta unit of biology and their relative composition in a tissue is the prime driver of bulk tissue gene expression variation. When there is no cell information, deconvolution is an effective tool to achieve cell resolution, which provides important information for learning disease complexity and its interactions with treatments, drugs and/or the environment in a wide variety of contexts. Here we present CytoPro, a production-level tissue and condition-specific deconvolution platform, based on a large collection of human tissue-specific signatures derived from single and sorted cells. CytoPro infer per-sample multiple cell-type composition, given input bulk gene expression. CytoPro includes a rigorous QC pipeline for learning, generating and selecting signatures and performs internal automated validation using multiple QC test criteria including: Comparison to ground truth cytometry and pure sorted cells data, performance evaluation using simulated data including robustness to noise as well as agreement with biological expectations in validation datasets regarding genes and cells. We demonstrate that CytoPro outperforms existing deconvolution tools, in both accuracy and robustness. By exploring multiple datasets with predefined disease phenotypes, and analyzing a use-case of biological treatment response, we show the ability of CytoPro to flush out relevant cell biology in real pathological conditions.

bioinformatics↗

bioRxiv2021

A personalized network framework reveals predictive axis of anti-TNF response across diseases

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach termed Disruption Networks to generate a new data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. The new data-type extends beyond the feature level space, to the relations space, by quantifying individual-level breaking or rewiring of cross-feature relations. Applying disruption network to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

systems biology↗

bioRxiv2020

Tumor-educated uncommitted hematopoietic stem cells promote a metastatic switch

Myeloid skewing of hematopoietic cells is a prominent promoter of metastasis. However, little is known about their education and differentiation pattern from hematopoietic stem and progenitor cells (HSPCs) during tumor progression and metastasis. Here we show that metastatic tumors dictate a unique differentiation pattern of HSPCs towards a specific myeloid progeny. Using single cell RNA-sequencing analysis integrated with proteomic screen of tumor secretome, we demonstrate that highly metastatic tumors dictate a long-lived differentiation of HSPCs towards monocyte-dendritic progenitors (MDPs) while low-metastatic tumors promote their differentiation into granulocyte-monocyte progenitors (GMPs). This effect is driven by IL-6 axis that is highly active in metastatic tumors. Consequently, loss and gain of function of IL-6 in tumor cells resulted in decreased and increased metastasis and corresponding MDP levels, respectively. Consistently, IL-6-educated MDPs but not GMPs obtained from highly metastatic tumors, adoptively transferred into mice bearing low metastatic tumors resulted in increased metastasis due to their further differentiation into immunosuppressive (M2) macrophages. Overall, our study reveals a new role for tumor-derived IL-6 that hijacks HSPC differentiation program towards myeloid cells that contribute to metastasis.

cancer biology↗

bioRxiv2018

Immune cellular homeostasis in early life is determined by genetic variants of cellular production and turnover

Complex physiological functionality is often the outcome of multiple interacting cell-types, yet mechanistically how a large number of trait-associated genes yield a single multi-cellular network governing the phenotype has not been well defined. Individuals immune-cellular profiles at homeostasis show high heritability and inter-individual variation with functional and clinical implications. We profiled immune cellular variation by mass-cytometry in 55 genetically diverse mouse strains. We identify 788 genes associated with cellular homeostasis, supporting a polygenic model where 52% of genes correspond to core homeostatic functions whose genetic variants suffice to predict phenotype. Trait genes form a multi-cellular network architecture showing increased functional complexity over evolutionary timescales for shared regulation to all cells, specialized cell-specific programs, and between-cell synchronization. Contrasting to human studies suggests the regulatory network expands with environmental exposure history. Our findings shed light on the origin of immune-cellular variation and regulatory architectures that may generalize to other environmentally sensitive systems.

immunology↗