Biology subjects

Krieger, M. S.

Publications and source records attributed to Krieger, M. S..

5 recordsLinked to original sources

bioRxiv2026

Inference and validation of causal genetic interactions in aging human fibroblasts

In the past hundred years, the fraction of fatalities due to age-related disease has risen from an envied minority of cases to the leading cause of death and economic morbidity in the developed world. Despite notable advances in understanding aging, its intrinsic complexity has prevented major therapeutic breakthroughs. This complexity extends across all systems and scales in an organism, leaving a "fingerprint of aging" across every aspect of cell and tissue behavior whose interrelationships are difficult to untangle. Compounding this problem, human aging is observed largely through cross-sectional rather than longitudinal data. Whether such data retain sufficient dynamical information to reveal causal interactions is unclear. Here we show that they can. Using convergent cross mapping (CCM), a method for identifying causal interactions in nonlinear dynamical systems, we reconstruct directed genetic interactions from age-ordered, cross-sectional transcriptomic data - recovering dynamics from populations rather than individuals. Applied to dermal fibroblasts from 490 GTEx donors, CCM identifies a genome-scale causal network whose topology, together with age-dependent changes in gene expression, predicts candidate drivers of aging. We tested these predictions experimentally by single-gene overexpression in fibroblasts from 55-year-old donors. Single-cell RNA sequencing confirmed 53.8% of testable CCM-predicted causal interactions, showing that dynamics reconstructed from cross-sectional populations contain experimentally recoverable information about gene-gene interactions. This 53.8% additionally exceeded that produced by multiple comparanda methods and a random null. We then asked whether this information could identify interventions that reverse cellular aging. Independent transcriptomic and high-dimensional morphological assays show that CCM-selected perturbations shift old fibroblasts toward states characteristic of young cells. These two measures of rejuvenation are strongly correlated, and the strongest interventions cause a majority of 55-year-old cells to resemble cells from 25-year-old donors. Thus, causal dynamics can be recovered from cross-sectional population data and used to identify interventions in aging. More generally, the results suggest a way to distinguish genes that change with age from genes that drive those changes, providing experimentally testable targets for altering the aging phenotype.

systems biology↗

bioRxiv2025

Interpretable gene network inference with nonlinear causality

Interactions among genes orchestrate the growth and survival of all living systems. Such interactions are mediated by vast networks of time-dependent couplings, yet existing strategies for inferring gene networks from omics data do not account for the strong causal constraints acting on nonlinear dynamical systems. As a result, we show here that current gene network inference approaches fail when applied to time series from physical systems. We thus introduce RiCE, a physics-based algorithm for inferring causal interaction graphs using geometric information embedded within time series. We benchmark RiCE against 30 other gene network inference methods, including recent probabilistic machine learning methods, and obtain leading results across 15 datasets spanning diverse experimental modalities and tissue types. RiCE learns physically-interpretable parameters of complex biological systems, requiring low overhead to achieve leading performance. We show diverse applications of RiCE: identifying transition states during the epithelial-mesenchymal transition, classifying cell subtypes during immune cell activation, and determining transcriptional dynamics during endocrinogenesis. Our work demonstrates nonlinearity as a critical inductive bias to accurately infer gene-gene interactions, as well as a key quantitative metric for understanding the dynamic biology of the cell.

systems biology↗

bioRxiv2019

A Novel, Versatile Speculum-free Callascope for Clinical Examination and Self-Visualization of the Cervix

BackgroundInvasive cervical cancer is preventable, yet affects 500,000 women worldwide each year, and over half these women die. Barriers to cervical cancer screening include lack of awareness of cervical cancer and the cervix, fear of the speculum, and lack of women-centric technologies. We developed a low-cost ([~]$50), cervix-imaging device called the Callascope, which comprises an imaging component, camera and inserter that eliminates the need for a speculum and enables self-insertion. We sought to assess the quality of physicians images of the cervix using the Callascope versus the speculum in live patients and study womens willingness to independently use the Callascope to image their cervix.\n\nMethodsWe conducted two main studies: (1) a clinical study in which a physician imaged the cervix of patients using both the speculum and Callascope in a 2x2 crossover design; and (2) home-based self-cervix imaging with the Callascope.\n\nResultsParticipants of the clinical study (n=28) and home study (n=12) all indicated greater comfort and an overall preference for the Callascope over the speculum. The clinical study data indicated that the Callascope enabled similar visualization compared to the speculum while significantly improving patient experience. With physician insertion and manipulation, the Callascope enabled cervix visualization for 82% of participants. In the home-study, 83% of participants were able to visualize their cervix with the Callascope on the first try and 100% after multiple attempts.\n\nConclusionThe Callascope is more comfortable and provides similar visualization to the speculum. The Callascope can be used by medical providers for clinical exams while also enabling home self-screening for cervical cancer and promoting a better understanding of ones cervix to increase awareness of cervical screening needs. The Callascope may increase cervical cancer screening rates through reducing barriers including cost, discomfort, lack of awareness and stigma.

clinical trials↗

bioRxiv2018

Novel cytokine interactions identified during perturbed hematopoiesis

Hematopoiesis is a dynamic process involving the up- and down-regulation of genes, as well as feed-back loops that stimulate or suppress circulating cytokine concentrations. More complete pictures of the gene regulatory networks that control the production of the blood system have emerged with the advent of single-cell sequencing techniques and refinements to the capabilities of immunoassays. However, information about the regulatory networks of cytokines is still lacking. A novel mathematical technique (convergent cross-mapping, or CCM) allows for the extraction of causal relationships from data, which is of crucial importance for understanding these networks. To reconstruct the cytokine networks within the hematopoietic system we measured the concentrations of 62 cytokines, platelets, and thrombopoietin from an individual with cyclic thrombocytopenia (regular oscillations in the megakaryocytes and platelets) over 84 days. Using CCM, we identified 61 previously unreported cytokine relationships. Our approach is the first broad-scale investigation into causal relationships between cytokines in the blood and suggests a new paradigm for understanding how dynamic regulation occurs during hematopoiesis.

immunology↗

bioRxiv2018

Long-term coexistence and regional heterogeneity of antibiotic-resistant infections reproduced by a simple spatial model

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

epidemiology↗