Biology subjects

Rodriguez, J. L.

Publications and source records attributed to Rodriguez, J. L..

7 recordsLinked to original sources

bioRxiv2025

Harnessing the CD2 axis to broaden and enhance the efficacy of CAR T cell therapies

Patients with T-cell lymphomas and leukemias have overall poor outcomes due to the lack of targeted and effective treatments, particularly in the relapsed and refractory settings. Development of chimeric antigen receptor (CAR) T-cells against T-cell neoplasms is limited by a lack of discriminating T-cell antigens that allow for effective anti-tumor responses while preventing CAR T-cell fratricide. We hypothesized that targeting CD2, a pan-T-cell antigen, using anti-CD2 CAR T-cells engineered without CD2 expression (CART2), would support CAR T-cell manufacturability and preclinical efficacy. Optimized CD2-knockout CART2, generated using CRISPR-Cas9, eradicated primary patient-derived CD2+ hematological neoplasms in vitro and in vivo, secreted effector cytokines, and exhibited adequate proliferative capacity. Nevertheless, CD2 has a key costimulatory function, and its deletion could lead to CAR T-cell dysfunction. Therefore, we tested the role of the CD2:CD58 axis in CAR T-cells, using the anti-CD19 CART models. We demonstrate that CD2 loss attenuates CART19 efficacy by reducing avidity for tumor antigen, co-stimulation, and ultimately in vivo activity. Analogously, we show that tumor CD58 loss reduces CART19 efficacy. To overcome this issue, we developed a novel PD-1:CD2 switch receptor that rescues intracellular CD2 signaling, particularly when PD-L1 is engaged, resulting in improved in vivo outcomes. Collectively, we studied the role of CD2 both as a target for CAR T cell therapy and as a critical costimulatory protein, whose signaling can be rescued using the PD-1:CD2 switch receptor. This receptor can be incorporated into CAR T-cells and provides an effective strategy to overcome CD2-signaling deficiencies.

cancer biology↗

bioRxiv2024

Large-scale RNA-seq mining reveals ciclopirox triggers TDP-43 cryptic exons

Nuclear clearance and cytoplasmic aggregation of TDP-43 in neurons, initially identified in ALS-FTD, are hallmark pathological features observed across a spectrum of neurodegenerative diseases. We previously found that TDP-43 loss-of-function leads to the transcriptome-wide inclusion of deleterious cryptic exons in brains and biofluids post-mortem as well as during the presymptomatic stage of ALS-FTD, but upstream mechanisms that lead to TDP-43 dysregulation remain unclear. Here, we developed a web-based resource (SnapMine) to determine the levels of TDP-43 cryptic exon inclusion across hundreds of thousands of publicly available RNA sequencing datasets. We established cryptic exon inclusion across a variety of human cells and tissues to provide ground truth references for future studies on TDP-43 dysregulation. We then explored studies that were entirely unrelated to TDP-43 or neurodegeneration and found that ciclopirox olamine (CPX), an FDA-approved antifungal, can trigger the inclusion of TDP-43-associated cryptic exons in a variety of mouse and human primary cells. CPX induction of cryptic exon occurs via heavy metal toxicity and oxidative stress, suggesting that similar vulnerabilities could play a role in neurodegeneration. Our work demonstrates how diverse datasets can be linked through common biological features and underscores that public archives of sequencing data represent a vastly underutilized resource with tremendous potential for uncovering novel insights into complex biological mechanisms and diseases.

neuroscience↗

bioRxiv2024

Armored Bicistronic CAR T Cells with Dominant-negative TGF-β Receptor II to Overcome Resistance in Glioblastoma.

Chimeric antigen receptor (CAR) T cells have shown significant efficacy in hematological diseases. However, CAR T therapy has demonstrated limited efficacy in solid tumors, including glioblastoma (GBM). One of the most important reasons is the immunosuppressive tumor microenvironment (TME), which promotes tumor growth and suppresses immune cells to eliminate tumor cells. The human transforming growth factor-beta (TGF-{beta}) plays a crucial role in forming the suppressive GBM TME and driving the suppression of the anti-GBM response. In order to mitigate TGF-{beta} mediated suppressive activity, we combined a dominant-negative TGF-{beta} receptor II (dnTGF{beta}RII) with our previous bicistronic CART-EGFR-IL13R2 construct, currently being evaluated in a clinical trial, to generate CART-EGFR-IL13R2-dnTGF{beta}RII, a tri-modular construct we are developing for clinical application. We hypothesized that this approach would more effectively subvert resistance mechanisms observed with GBM. Our data suggests that CART-EGFR-IL13R2-dnTGF{beta}RII significantly augmented T cell proliferation and enhanced functional responses, particularly in a TGF{beta}-rich tumor environment. Additionally, in vivo studies validated the safety and efficacy of the dnTGF{beta}RII cooperating with CARs in targeting and eradicating GBM in a NSG mouse model.

cancer biology↗

bioRxiv2022

Selective inhibitors of JAK1 targeting a subtype-restricted allosteric cysteine

The JAK family of non-receptor tyrosine kinases includes four subtypes (JAK1, JAK2, JAK3, and TYK2) and is responsible for signal transduction downstream of diverse cytokine receptors. JAK inhibitors have emerged as important therapies for immuno(onc)ological disorders, but their use is limited by undesirable side effects presumed to arise from poor subtype selectivity, a common challenge for inhibitors targeting the ATP-binding pocket of kinases. Here, we describe the chemical proteomic discovery of a druggable allosteric cysteine present in the non-catalytic pseudokinase domain of JAK1 (C817) and TYK2 (C838), but absent from JAK2 or JAK3. Electrophilic compounds selectively engaging this site block JAK1-dependent transphosphorylation and cytokine signaling, while appearing to act largely as "silent" ligands for TYK2. Importantly, the allosteric JAK1 inhibitors do not impair JAK2-dependent cytokine signaling and are inactive in cells expressing a C817A JAK1 mutant. Our findings thus reveal an allosteric approach for inhibiting JAK1 with unprecedented subtype selectivity.

pharmacology and toxicology↗

bioRxiv2021

Convergent antibody responses to the SARS-CoV-2 spike protein in convalescent and vaccinated individuals

Unrelated individuals can produce genetically similar clones of antibodies, known as public clonotypes, which have been seen in responses to different infectious diseases as well as healthy individuals. Here we identify 37 public clonotypes in memory B cells from convalescent survivors of SARS-CoV-2 infection or in plasmablasts from an individual after vaccination with mRNA-encoded spike protein. We identified 29 public clonotypes, including clones recognizing the receptor-binding domain (RBD) in the spike protein S1 subunit (including a neutralizing, ACE2-blocking clone that protects in vivo), and others recognizing non-RBD epitopes that bound the heptad repeat 1 region of the S2 domain. Germline-revertant forms of some public clonotypes bound efficiently to spike protein, suggesting these common germline-encoded antibodies are preconfigured for avid recognition. Identification of large numbers of public clonotypes provides insight into the molecular basis of efficacy of SARS-CoV-2 vaccines and sheds light on the immune pressures driving the selection of common viral escape mutants.

immunology↗

bioRxiv2021

Deep Directed Evolution of Solid Binding Peptides for Quantitative Big-Data Generation

Proteins have evolved over millions of years to mediate and carry-out biological processes efficiently. Directed evolution approaches have been used to genetically engineer proteins with desirable functions such as catalysis, mineralization, and target-specific binding. Next-generation sequencing technology offers the capability to discover a massive combinatorial sequence space that is costly to sample experimentally through traditional approaches. Since the permutation space of protein sequence is virtually infinite, and evolution dynamics are poorly understood, experimental verifications have been limited. Recently, machine-learning approaches have been introduced to guide the evolution process that facilitates a deeper and denser search of the sequence-space. Despite these developments, however, frequently used high-fidelity models depend on massive amounts of properly labeled quality data, which so far has been largely lacking in the literature. Here, we provide a preliminary high-throughput peptide-selection protocol with functional scoring to enhance the quality of the data. Solid binding dodecapeptides have been selected against molybdenum disulfide substrate, a two-dimensional atomically thick semiconductor solid. The survival rate of the phage-clones, upon successively stringent washes, quantifies the binding affinity of the peptides onto the solid material. The method suggested here provides a fast generation of preliminary data-pool with [~]2 million unique peptides with 12 amino-acids per sequence by avoiding amplification. Our results demonstrate the importance of data-cleaning and proper conditioning of massive datasets in guiding experiments iteratively. The established extensive groundwork here provides unique opportunities to further iterate and modify the technique to suit a wide variety of needs and generate various peptide and protein datasets. Prospective statistical models developed on the datasets to efficiently explore the sequence-function space will guide towards the intelligent design of proteins and peptides through deep directed evolution. Technological applications of the future based on the peptide-single layer solid based bio/nano soft interfaces, such as biosensors, bioelectronics, and logic devices, is expected to benefit from the solid binding peptide dataset alone. Furthermore, protocols described herein will also benefit efforts in medical applications, such as vaccine development, that could significantly accelerate a global response to future pandemics.

molecular biology↗

bioRxiv2019

A Generalized Similarity Metric for Peptide Binding Affinity Prediction

The ability to capture the relationship between similarity and functionality would enable the predictive design of peptide sequences for a wide range of implementations from developing new drugs to molecular scaffolds in tissue engineering and biomolecular building blocks in nanobiotechnology. Similarity matrices are widely used for detecting sequence homology but depend on the assumption that amino acid mutational frequencies reflected by each matrix are relevant to the system in which they are applied. Increasingly, neural networks and other statistical learning models solve problems related to functional prediction but avoid using known features to circumvent unconscious bias. We demonstrated an iterative alignment method that enhances predictive power of similarity matrices based on a similarity metric, the Total Similarity Score. A generalized method is provided for application to amino acid sequences from inorganic and organic systems by benchmarking it on the debut quartz-binder set and 3 peptide-protein sets from the Immune Epitope Database. Pearson and Spearman Rank Correlations show that by treating the gapless Total Similarity Score as a predictor of relative binding affinity, prediction of test data has a 0.5-0.7 Pearson and Spearman Rank correlation. with respect to size of the dataset. Since the benchmarks used herein are from a solid-binding peptide and a protein-peptide system, our proposed method could prove to be a highly effective general approach for establishing the predictive sequence-function relationships of among the peptides with different sequences and lengths in a wide range of biotechnology, nanomedicine and bioinformatics applications.\n\nAuthor SummaryThe significance of this work is to expand the applicability of a known metric for describing the function of tiny proteins also called peptides. The Total Similarity Score (TSS) can describe how similar a peptide, or a group of peptides are to another group of sequences with a known or suspected function. A peptide/group of peptides will always have a high TSS if it contains the same or similar amino acids in the same positions. This metric can therefore be used to select peptides for useful functions based purely on conserved amino acids in unknown positions. The greedy search algorithm used to learn how similar amino acids are to each other has been shown to be marginally effective in this larger dataset. Therefore, we argue that the TSS metric is a highly useful one for predicting peptide affinity but a different machine learning algorithm should be applied to make full use of it.

bioinformatics↗