Biology subjects

Katz, R.

Publications and source records attributed to Katz, R..

8 recordsLinked to original sources

bioRxiv2026

Designed nanofiber immunogens prime antibody responsesto diverse conformations of the HIV-1 fusion peptide

Focusing antibody responses towards known neutralizing epitopes is a major strategy for protective immunization. Targeting flexible antigenic regions represents a unique challenge because functional antibodies must be able to bind the epitope of interest in multiple conformations. The fusion peptide (FP) of HIV-1 is a flexible peptide critical to viral entry of host cells for which broadly neutralizing antibodies have been identified. Because elicitation of potent, functional antibodies toward this epitope remains an unmet challenge in HIV-1 vaccination, we introduce a designed immunogen platform capable of raising anti-FP-antibodies with increased conformational tolerance relative to a traditional carrier protein. Antibodies induced by these immunogens bind heterologous FP sequences when elicited in mice and infant non-human primates. This platform shows promise for preclinical development to prime antibody responses toward FP of the HIV-1 envelope and can likely be translated to peptide immunofocusing for other viral pathogens.

bioengineering↗

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

neuroscience↗

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

neuroscience↗

bioRxiv2023

Synthetic Hydrogels with Entangled Neutrophil Extracellular Traps Influence Tumor Progression in MDA-MB-231 Cells

We incorporated neutrophil extracellular traps (NETs) in a poly(ethylene glycol)-based synthetic extracellular matrix to study their impact on tumorigenesis in triple negative breast carcinoma (TNBC) cells in a highly controlled environment. We observed that NETs helped to increase cell survival, proliferation, and pro-metastatic morphological phenotype. We also showed that the presence of NETs influenced the secretion of IL-8, a pro-NETosis factor, and that conditioned media from cells cultured in these gels influenced NETosis in an IL-8 dependent manner. The results observed in this system correlate with murine models and clinical studies in the literature and help to provide additional insight of the individual factors at play in the NET-mediated promotion of TNBC progression and metastasis.

bioengineering↗

bioRxiv2022

Translation in the cell under fierce competition for shared resources: a mathematical model

During the process of translation the mRNAs in the cell "compete" for shared resources like tRNA molecules and ribosomes. This creates an indirect and intricate coupling between the mRNAs. For example, if ribosomal "traffic jams" evolve on some mRNA then the abundance of free ribosomes may decrease leading to lower initiation rates in the other mRNAs. When the shared resources are abundant the coupling between mRNAs due to this competition is weak. However, when the resources are scarce, e.g., when the pool of free ribosomes is starved, the competition may have a dramatic effect on the dynamics of translation in the cell. This scenario may be relevant for example under stress conditions or during a high yield viral infection, where the viral mRNAs "hijack" components of the translation machinery. Fierce competition for shared resources may also take place in synthetic or engineered systems such as cell free systems or in the case of high-throughput heteroglougs gene expression. We study this scenario using a mathematical model that includes a network of m ribosome flow models (RFMs) interconnected via a pool of free ribosomes. Each RFM is a non-linear dynamical model for ribosome flow along a single mRNA molecule, and the interconnection via the pool encapsulates the competition for shared resources. We analyze the case where m is large, i.e., a there is a large number of mRNAs. This implies that many ribosomes are attached to the mRNAs and thus the pool is starved. Our model allows quantitative and qualitative analysis of the network steady state when the pool is starved. Our analysis results show that adding an mRNA to the network always decreases the steady state pool density. This makes sense, as every new mRNA "consumes" ribosomes. We also show that adding an mRNA has an intricate effect on the total protein production in the network: on the one-hand, the new mRNA produces new proteins. On the other-hand, the other mRNAs produce less proteins, as the pool that feeds these mRNAs now has a smaller abundance of ribosomes. Our analysis yields an explicit bound for the total production rate of the network when the number of RFMIOs is very large. In particular, we analyze how the total density of ribosomes in the network bounds the total production rate. This bound demonstrates that when the number of mRNAs increases, the marginal utility of adding another mRNA diminishes, and the total protein production rate saturates to a limiting value. We demonstrate our analysis approach using an example of producing insulin in a cell free system.

systems biology↗

bioRxiv2022

PRR14 organizes H3K9me3-modifed heterochromatin at the nuclear lamina

The eukaryotic genome is organized in three dimensions within the nucleus. Transcriptionally active chromatin is spatially separated from silent heterochromatin, a large fraction of which is located at the nuclear periphery. However, the mechanisms by which chromatin is localized at the nuclear periphery remain poorly understood. Here we demonstrate that Proline Rich 14 (PRR14) protein specifically organizes H3K9me3-modified heterochromatin at the nuclear lamina. We show that PRR14 dynamically associates with both the nuclear lamina and heterochromatin, and is able to reorganize heterochromatin in the nucleus of interphase cells independent of mitosis. We demonstrate that PRR14 can bind all isoforms of heterochromatin protein 1 (HP1). We characterize two functional HP1-binding sites within PRR14 that contribute to its association with heterochromatin. Results of fluorescent recovery after photobleaching (FRAP) and super-resolution imaging indicate that PPR14 forms an anchoring surface for heterochromatin at the nuclear lamina where it interacts dynamically with HP1-associated, H3K9me3-modified chromatin. Our study reveals the mechanism through which PRR14 tethers heterochromatin to the nuclear lamina and we propose a model of dynamic heterochromatin organization at the nuclear periphery.

cell biology↗

bioRxiv2018

Personal clinical history predicts antibiotic resistance in urinary tract infections

The prevalence of antibiotic resistance in urinary tract infections (UTIs) often renders the prescribed antimicrobial treatment ineffective, highlighting the need for personalized prediction of resistance at time of care. Here, crossing a 10-year longitudinal dataset of over 700,000 community-acquired UTIs with over 6,000,000 personally-linked records of antibiotic purchases, we show that the resistance profile of infections can be predicted based on patient-specific demographics and clinical history. Age, gender, and retirement home residence had strong, yet differential and even non-monotonic, associations with resistance to different antibiotics. Resistance profiles were also associated with the patients records of past urine samples and antibiotic usage, with these associations persisting for months and even longer than a year. Drug usage selected specifically for its own cognate resistance, which led indirectly, through genetic linkage, also to resistance to other, even mechanistically unrelated, drugs. Applying machine learning models, these association patterns allowed good personalized predictions of resistance, which could inform and better optimize empirical prescription of antibiotics.

epidemiology↗