Biology subjects

Kaufman, B.

Publications and source records attributed to Kaufman, B..

10 recordsLinked to original sources

bioRxiv2026

Spatially enforced engagement of PD-1⁺ T cells with AXL⁺ cells reprograms the tumor microenvironment and overcomes anti-PD-1 resistance

Background: AXL overexpression promotes tumor-cell plasticity, local immune suppression, and resistance to PD-1 blockade. Conventional anti-AXL and anti-PD-1 antibodies combinations act independently and do not ensure physical proximity between PD-1+ T cells and AXL+ cells within the immunosuppressive tumor microenvironment. Methods: To address this limitation, we developed a non-tandem, tetravalent IgG bispecific antibody designed to simultaneously engage PD-1 and AXL (BiTE-AXL/PD-1). Its antitumor activity was evaluated using in vitro functional assays and humanized and immunocompetent head and neck squamous cell carcinoma (HNSCC) models and compared with the combination of monospecific anti-AXL and anti-PD-1 antibodies. Spatial multiplex imaging was used to characterize treatment-associated changes in the tumor microenvironment of an anti-PD-1-resistant murine model. Results: BiTE-AXL/PD-1 enhanced T-cell activation, promoted AXL-dependent tumor-T-cell conjugation, and increased tumor-cell killing in vitro, outperforming the combination of the two monospecific antibodies. In humanized and immunocompetent HNSCC models, BiTE treatment significantly delayed tumor growth compared with the antibody combination and was well tolerated, with no overt toxicity. Spatial multiplex imaging revealed a shift from a fibroblast-dominated microenvironment characterized by exhausted CD8+ T cells toward dendritic-cell- and CD8+ T-cell-enriched niches associated with antitumor immune activity. Conclusion: BiTE-AXL/PD-1 promotes physical proximity between PD-1+ T cells and AXL+ cells, enhances antitumor immune responses, and remodels the immunosuppressive tumor microenvironment. These findings support PD-1/AXL bispecific targeting as a promising therapeutic strategy to overcome resistance to PD-1 blockade in HNSCC.

cancer biology↗

bioRxiv2026

Development and evaluation of a cost-effective, mid-density SNP array as a sorghum community genotyping resource

The development of accessible and cost-effective genotyping platforms is essential to accelerate genetic gain in crop improvement. To address the U.S. sorghum communitys need for a standardized, mid-density genotyping resource, we developed and validated a targeted single-nucleotide polymorphism (SNP) array using the PlexSeq next-generation sequencing (NGS) platform. The resulting genotyping array includes 2,421 SNPs spanning all ten Sorghum bicolor chromosomes and integrates trait-linked and quality control markers selected by public and private stakeholders. Genotyping 2,726 diverse accessions, including the Sorghum Association Panel (SAP), demonstrated high call rates (>90% for most samples and markers), low missing data, and accurate resolution of population structure consistent with prior whole-genome studies. In comparative genomic prediction analyses, the mid-density array performed equivalently to high-density genotype-by-sequencing (GBS) platforms for key traits such as grain yield and plant height across multi-environment trials. Designed for broad utility in breeding pipelines, the array enables marker-assisted selection, genomic prediction, identity verification, and germplasm quality control. Moreover, its adoption by the USDA National Plant Germplasm System facilitates the curation of genebanks and the management of core collections. This community-driven genotyping platform offers a scalable, reproducible, and customizable tool to support molecular breeding in sorghum and underscores the value of targeted marker systems in resource-optimized crop improvement programs.

plant biology↗

bioRxiv2024

Latent Diffusion For Conditional Generation of Molecules

AO_SCPLOWBSTRACTC_SCPLOWDesigning a small molecule therapeutic is a challenging multi-parameter optimization problem. Key properties, such as potency, selectivity, bioavailability, and safety must be jointly optimized to deliver an effective clinical candidate. We present COATI-LDM, a novel application of latent diffusion models to the conditional generation of property-optimized, drug-like small molecules. Diffusive generation of latent molecular encodings, rather than direct diffusive generation of molecular structures, offers an appealing way to handle the small and mismatched datasets that are common for molecular properties. We benchmark various diffusion guidance schemes and sampling methods against a pre-trained autoregressive transformer and genetic algorithms to evaluate control over potency, expert preference, and various physicochemical properties. We show that conditional diffusion allows control over the properties of generated molecules, with practical and performance advantages over competing methods. We also apply the recently introduced idea of particle guidance to enhance sample diversity. We prospectively survey a panel of medicinal chemists and determine that we can conditionally generate molecules that align with their preferences via a learned preference score. Finally, we present a partial diffusion method for the local optimization of molecular properties starting from a seed molecule. Conditional generation of small molecules using latent diffusion models on molecular encodings provides a highly practical and flexible alternative to prior molecular generation schemes.

bioinformatics↗

bioRxiv2024

TCA Cycle Dysfunction and Amino Acid Catabolism Drive Hepatic Steatosis in Mice with HFpEF

The prevalence of cardiometabolic heart failure with preserved ejection fraction (HFpEF) continues to grow worldwide, and now represents over half of current heart failure cases in the United States (1). Due to a lack of specific approved therapies, current treatment guidelines focus on the management of comorbidities related to metabolic syndrome (e.g. obesity, diabetes, hypertension) that promote HFpEF progression (1). The same comorbidities also drive cardiometabolic disease in non-cardiac tissues, and links between disease presentations in different organs are increasingly being recognized in the clinic. However, mechanistic studies examining the underlying pathophysiological connections have not kept pace, particularly in the cardio-hepatic disease axis (2). To address this, we used a recently developed and validated preclinical model of HFpEF (3) to examine how this disease impacts the liver. The development of HFpEF in mice leads to the simultaneous development of widespread hepatic steatosis that is consistent with human non-alcoholic fatty liver disease (NAFLD). Mechanistically, we show that the liver steatosis observed is driven by excess glucogenic amino acid entry into the TCA cycle, which promotes hepatic glucose production and de novo lipogenesis. Our findings suggest that HFpEF development is a multi-organ event, with implications for both preclinical and translational research.

physiology↗

bioRxiv2023

Functional binding of PD1 ligands predicts response to anti-PD1 treatment in cancer patients

Accurate predictive biomarkers of response to immune checkpoint inhibitors (ICIs) are required for better stratifying cancer patients to ICI treatments. Here, we present a new concept for a bioassay to predict the response to anti-PD1 therapies, which is based on measuring the binding functionality of PDL1 and PDL2 to their receptor, PD1. In detail, we developed a cell-based reporting system, called the Immuno-checkpoint Artificial Reporter with overexpression of PD1 (IcAR-PD1) and evaluated the PDL1 and PDL2 binding functionality in tumor cell lines, patient-derived xenografts, and in fixed-tissue tumor samples obtained from cancer patients. In a retrospective clinical study, we found that the functionality of PDL1 and PDL2 predicts response to anti-PD1, and functionality of PDL1 binding is a more effective predictor than PDL1 protein expression alone. Our findings suggest that assessing the functionality of ligand binding is superior to staining of protein expression for predicting response to ICIs. TeaserPositive clinical response of cancer patients to anti-PD1 therapy can be predicted by measuring the binding activity of PDL1 and PDL2.

immunology↗

bioRxiv2023

Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation

Emerging evidence reinforce the role of non-genetic adaptive resistance to chemotherapy, that involves rewiring of transcriptional programs in surviving tumors. We combined longitudinal transcriptomics with temporal pattern analysis to dissect patient-specific emergence of resistance in breast cancer. Matched triplets of tumor biopsies (pre-treatment, post-treatment and adjacent normal) were collected from breast cancer patients who received neo-adjuvant chemotherapy. Full transcriptome was analyzed by longitudinal pattern classification to follow patient-specific expression modulations. We found that dynamics of gene expression dictates resistance-related modulations. The results unraveled important principles in emergence of adaptive resistance: 1. Genes with resistance patterns are already dysregulated in the primary tumor, supporting a primed drug-tolerant state. 2. In each patient, multiple resistance-related genes are rewired but converge into few dysregulated modules. 3. Rewiring of diverse genes and pathway dysregulation vary among individuals who receive the same treatments. Patient-specific chemoresistome maps disclosed tumors acquired resistance and exposed their vulnerabilities. Mapping the complexity of dysregulated pathways in individual patients revealed important insights on adaptive resistance mechanisms. To survive the toxic drug effect, tumor cells either sustain a drug-tolerant state or intensify it, specifically bypassing the drugs interference. Depicting an individual road map to resistance can offer personalized therapeutic strategies.

cancer biology↗

bioRxiv2022

Liraglutide protects against diastolic dysfunction and improves ventricular protein translation

PurposeDiastolic dysfunction is an increasingly common cardiac pathology linked to heart failure with preserved ejection fraction. Previous studies have implicated glucagon-like peptide 1 (GLP-1) receptor agonists as potential therapies for improving diastolic dysfunction. In this study, we investigate the physiologic and metabolic changes in a mouse model of angiotensin II (AngII) mediated diastolic dysfunction with and without the GLP-1 receptor agonist liraglutide (Lira). MethodsMice were divided into sham, AngII, or AngII + Lira therapy for 4 weeks. Mice were monitored for cardiac function, weight change, and blood pressure at baseline and after 4 weeks of treatment. After 4 weeks of treatment, tissue was collected for histology, protein analysis, targeted metabolomics, and protein synthesis assays. ResultsAngII treatment causes diastolic dysfunction when compared to sham mice. Lira partially prevents this dysfunction. The improvement in function in Lira mice is associated with dramatic changes in amino acid accumulation in the heart. Lira mice also have improved markers of protein translation by Western blot and increased protein synthesis by puromycin assay, suggesting that increased protein turnover protects against fibrotic remodeling and diastolic dysfunction seen in the AngII cohort. Lira mice also lost lean muscle mass compared to the AngII cohort, raising concerns about peripheral muscle scavenging as a source of the increased amino acids in the heart. ConclusionsLira therapy protects against AngII-mediated diastolic dysfunction, at least in part by promoting amino acid uptake and protein turnover in the heart. Liraglutide therapy is associated with loss of mean muscle mass, and long-term studies are warranted to investigate sarcopenia and frailty with liraglutide therapy in the setting of diastolic disease.

physiology↗

bioRxiv2021

Mining divergent and conserved signals in 5' splicing site sequences

The main steps of the splicing process are similar across eukaryotes. However differences in splicing factors, gene architecture and sequence divergences suggest clade-specific features of splicing and its regulation. In each organism the ensemble of 5 splicing sequences reflects the balance between natural nucleotidic variability and minimal molecular constraints to assure splicing fidelity. This compromise shapes the underlying statistical patterns in donor sequences composition. In this work we aimed to mine conserved and divergent signals in splicing donor sequences. As 5 donor sequences are a major cue for proper recognition of splicing sites we reasoned that statistical regularities of their sequence composition might reflect biological functionality and evolutionary history associated to splicing mechanisms. We considered a regularized maximum entropy modeling framework to mine for non-trivial two-site correlations in donor sequences of 30 different eukaryote organisms. Our approach allowed us to accommodate and extend within a unified framework many of the regularities observed in previous works, like the negative epistatic effects between exonic and intronic consensus sites. In addition, for each analyzed organism, we could identify minimal sets of two-site coupling patterns that could generate, at a given regularization level, observed one-site and two-site frequencies in donor sequences. Noticeably, performing a systematic and comparative analysis of 5ss we showed that lineage information could be traced from joint di-nucleotide probabilities. Specifically, we could identify characteristic two-site coupling patterns for plants and for animals and argue that they could echo differences in splicing regulation previously reported between these groups. Author summaryThe sequence composition of 5 splicing sites of eukaryote organisms reflect a complex scenario. Nucleotide variability has to coexist with the need to correctly define exon/intron boundaries and the fidelity of splicing seems to depend on a pattern of trade-offs between substitutions at different positions of the splicing site. Better understanding of these patterns may help to gain insight into the details underlying the splicing process and its evolution. In this work we aimed to study conserved and divergent signatures embedded in the sequence composition of eukaryotic 5 splicing sites. We developed generative probabilistic models that allowed us to analyze sequence composition of donor sequences for several eukaryote organisms. Our regularized models served to incrementally disentangle the minimal set of coupling parameters needed to accurately reproduced observed 1-site and 2-site nucleotide frequencies. Focusing our study on di-nucelotide probabilities we found that they actually carried phylogenetic signal. In particular, our comparative analysis allowed us to identify differential two-site coupling patterns for animal and plants that might be related to specific differences in splicing regulation.

bioinformatics↗

bioRxiv2020

An automated, high throughput methodology optimized for quantitative cell-free mitochondrial and nuclear DNA isolation from plasma

Circulating, cell-free mitochondrial DNA (ccf-mtDNA) and nuclear DNA (ccf-nDNA) are under investigation as biomarkers for various diseases. Optimal ccf-mtDNA isolation parameters, like those outlined for ccf-nDNA, have not been established. Here, we optimized a protocol for both ccf-mtDNA and ccf-nDNA recovery using a magnetic bead-based isolation process on an automated 96-well platform. Using the optimized protocol, our data show 6-fold improved yields of ccf-mtDNA when compared to the starting protocol. Digestion conditions, liquid handling characteristics, and magnetic particle processor programming all contributed to increased recovery and improved reproducibility. To our knowledge, this is the first high-throughput approach optimized for mtDNA and nDNA recovery and serves as an important starting point for clinical studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/206987v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@13ab547org.highwire.dtl.DTLVardef@1535020org.highwire.dtl.DTLVardef@b191aaorg.highwire.dtl.DTLVardef@1d23fa4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2018

G-quadruplex dynamics contribute to epigenetic regulation of mitochondrial function

Single-stranded DNA or RNA sequences rich in guanine (G) can adopt non-canonical structures known as G-quadruplexes (G4). Predicted G4-forming sequences in the mitochondrial genome are enriched on the heavy-strand and have been associated with formation of deletion breakpoints that cause mitochondrial disorders. However, the functional roles of G4 structures in regulating mitochondrial respiration in non-cancerous cells remain unclear. Here, we demonstrate that RHPS4, previously thought to be a nuclear G4-ligand, localizes primarily to mitochondria in live cells by mechanisms involving mitochondrial membrane potential. We find that RHPS4 exposure causes an acute inhibition of mitochondrial transcript elongation, leading to respiratory complex depletion. At higher ligand doses, RHPS4 causes mitochondrial DNA (mtDNA) replication pausing and genome depletion. Using these different levels of RHPS4 exposure, we describe discrete nuclear gene expression responses associated with mitochondrial transcription inhibition or with mtDNA depletion. Importantly, a mtDNA variant with increased anti-parallel G4-forming characteristic shows a stronger respiratory defect in response to RHPS4, supporting the conclusion that mitochondrial sensitivity to RHPS4 is G4-structure mediated. Thus, we demonstrate a direct role for G4 perturbation in mitochondrial genome replication, transcription processivity, and respiratory function in normal cells and describe the first molecule that differentially recognizes G4 structures in mtDNA.

molecular biology↗