Biology subjects

Shapiro, R.

Publications and source records attributed to Shapiro, R..

7 recordsLinked to original sources

bioRxiv2026

Tumor-tropic E. coli engineered as living T and NK cell engagers

Despite advances in immunotherapy, most solid tumors remain resistant to treatment. Immune cell engagers redirect cytotoxic lymphocytes against cancer, but limited tumor access, immunosuppressive microenvironments and systemic immune activation limit efficacy. Here we develop live immune modulating engagers (LIME), a modular platform where non-pathogenic, tumor-tropic Escherichia coli display tandem single-chain variable fragments targeting a tumor-associated antigen and an activating receptor on T or natural killer cells. LIME bridged effector and tumor cells, induced transcriptional programs of T cell activation, metabolism and proliferation, and enhanced cytotoxicity across cancer cell lines and patient-derived organoids. In mouse models, LIME safely accumulated in tumors, outperformed tarlatamab in small cell lung cancer, and induced durable immunity in lymphoma. RAS inhibition and PD-L1 blockade enhanced LIME activity in pancreatic cancer and induced humoral responses. Multi-lineage immune modulation remained tumor-confined, without organ toxicity. These findings establish LIME as a versatile living therapeutic platform for programmable, tumor-restricted immune orchestration.

bioengineering↗

bioRxiv2024

FungAMR: A comprehensive portrait of antimicrobial resistance mutations in fungi

Antimicrobial resistance (AMR) is a global threat. To optimize the use of our antifungal arsenal, we need rapid detection and monitoring tools that rely on high-quality AMR mutation data. Here, we performed a thorough manual curation of published AMR mutations in fungal pathogens to produce the FungAMR reference dataset. A total of 501 papers were curated, leading to 35,792 mutation entries all classified with the degree of evidence that supports their role in resistance. FungAMR covers 95 species, 246 genes and 208 drugs. We combined variant effect predictors with FungAMR resistance mutations and showed that these tools could be used to help predict the potential impact of mutations on AMR. Additionally, a comparative analysis among species revealed a high level of convergence in the molecular basis of resistance, highlighting some potentially universal resistance mutations. The analysis also showed that a significant number of resistance mutations lead to cross-resistance within antifungals of a class, as well as between classes for certain mutated genes. The acquisition of fungal resistance in the clinic and the field is an urging concern. Finally, we provide a computational tool, ChroQueTas, that leverages FungAMR to screen fungal genomes for AMR mutations. These resources are anticipated to have great utility for researchers in the fight against antifungal resistance.

microbiology↗

bioRxiv2023

Deep-learning Based Pathological Assessment of Frozen Procurement Kidney Biopsies Predicts Graft Loss and Guides Organ Utilization: A Large-scale Retrospective Study

BackgroundLesion scores on procurement donor biopsies are commonly used to guide organ utilization. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropriate discard. MethodsWe constructed deep-learning based models to recognize kidney tissue compartments in H&E stained sections from procurement biopsies performed at 583 hospitals nationwide in year 2011-2020. The models were trained and tested respectively on 11473 and 3986 images sliced from 100 slides. We then extracted whole-slide abnormality features from 2431 kidneys, and correlated with pathologists scores and transplant outcomes. Finally, a Kidney Donor Quality Score (KDQS) incorporating digital features and the Kidney Donor Profile Index (KDPI) was derived and used in combination with recipient demographic and peri-transplant characteristics to predict graft loss or assist organ utilization. ResultsOur model accurately identified 96% and 91% of normal/sclerotic glomeruli respectively; 94% of arteries/arterial intimal fibrosis regions; 90% of tubules. Three whole-slide features (Sclerotic Glomeruli%, Arterial Intimal Fibrosis%, and Interstitial Fibrosis%) demonstrated strong correlations with corresponding pathologists scores (n=2431), but had superior associations with post-transplant eGFR (n=2033) and graft loss (n=1560). The combination of KDQS and other factors predicted 1- and 4-year graft loss (discovery: n=520, validation: n=1040). Finally, by matching 398 discarded kidneys due to "biopsy findings" to transplanted population, the matched transplants from discarded KDQS<4 group (110/398, 27.6%) showed similar graft survival rate to unmatched transplanted kidneys (2-, 5-year survival rate: 97%, 86%). KDQS [≥] 7 (37/398, 9.3%) and 1-year survival model score [≥] 0.55 were determined to identify possible discards (PPV=0.92). ConclusionThis deep-learning based approach provides automatic and reliable pathological assessment of procurement kidney biopsies, which could facilitate graft loss risk stratification and organ utilization. Translational StatementThis deep-learning based approach provides rapid but more objective, sensitive and reliable assessment of deceased-donor kidneys before transplantation, and improves the prognostic value of procurement biopsies, thus could potentially reduce inappropriate discard and stratify patients needing monitoring or preventative measures after transplantation. The pipeline can be integrated into various types of scanners and conveniently generates report after slide scanning. Such report can be used in conjunction with pathologists report or independently for centers lacking renal pathologists.

bioinformatics↗

bioRxiv2023

Sustained In Vivo Perfusion of a Re-Endothelialized Tissue Engineered Kidney Graft in a Human-Scale Animal Model

IntroductionDespite progress in whole-organ decellularization and recellularization, maintaining long-term perfusion in vivo remains a hurdle to realizing clinical translation of bioengineered kidney grafts. The objectives for the present study were to define a threshold glucose consumption rate (GCR) that could be used to predict in vivo graft hemocompatibility and utilize this threshold to assess the in vivo performance of clinically relevant decellularized porcine kidney grafts recellularized with human umbilical vein endothelial cells (HUVECs). Materials and MethodsTwenty-two porcine kidneys were decellularized and 19 were re-endothelialized using HUVECs. Functional revascularization of control decellularized (n=3) and re-endothelialized porcine kidneys (n=16) was tested using an ex vivo porcine blood flow model to define an appropriate metabolic glucose consumption rate (GCR) threshold above which would sustain patent blood flow. Re-endothelialized grafts (n=9) were then transplanted into immunosuppressed pigs with perfusion measured using angiography post-implant and on days 3 and 7 with 3 native kidneys used as controls. Patent recellularized kidney grafts underwent histological analysis following explant. ResultsThe glucose consumption rate of recellularized kidney grafts reached a peak of 41.3{+/-}10.2 mg/hour at 21{+/-}5 days, at which point the grafts were determined to have sufficient histological vascular coverage with endothelial cells. Based on these results, a minimum glucose consumption rate threshold of 20 mg/hour was set. The revascularized kidneys had a mean perfusion percentage of 87.7{+/-}10.3%, 80.9{+/-}33.1%, and 68.5{+/-}38.6% post-reperfusion on Days 0, 3 and 7, respectively. The 3 native kidneys had a mean post-perfusion percentage of 98.4{+/-}1.6%. These results were not statistically significant. ConclusionThis study is the first to demonstrate that human-scale bioengineered porcine kidney grafts developed via perfusion decellularization and subsequent re-endothelialization using HUVEC can maintained patency with consistent blood flow for up to 7 days in vivo. These results lay the foundation for future research to produce human-scale recellularized kidney grafts for transplantation.

bioengineering↗

bioRxiv2020

Methodological Issues with Search in MEDLINE: A Longitudinal Query Analysis

This study compares the results of data collected from a longitudinal query analysis of the MEDLINE database hosted on multiple platforms that include PubMed, EBSCOHost, Ovid, ProQuest, and Web of Science in order to identify variations among the search results on the platforms after controlling for search query syntax. We devised twenty-nine sets of search queries comprised of five queries per set to search against the five MEDLINE database platforms. We ran our queries monthly for a year and collected search result count data to observe changes. We found that search results vary considerably depending on MEDLINE platform, both within sets and across time. The variation is due to trends in scholarly publication that include publishing online first versus publishing in journal issues, which leads to metadata differences in the bibliographic record; to differences in the level of specificity among search fields provided by the platforms; to database integrity issues that lead to large fluctuations in monthly search results based on the same query; and to database currency issues that arise due to when each platform updates its MEDLINE file. Specific bibliographic databases, like PubMed and MEDLINE, are used to inform clinical decision-making, create systematic reviews, and construct knowledge bases for clinical decision support systems. Since they serve as essential information retrieval and discovery tools that help identify and collect research data and are used in a broad range of fields and as the basis of multiple research designs, this study should help clinicians, researcher, librarians, informationalists, and others understand how these platforms differ and inform future work in their standardization.

bioinformatics↗

bioRxiv2019

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Image-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

pathology↗

bioRxiv2017

Mapping the drivers of within-host pathogen evolution using massive data sets

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Identifying such interactions can potentially be achieved through genetic association studies. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. To address these problems, we have developed a Bayesian approach for detecting host influences on pathogen evolution that makes use of vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Using simulations and empirical analysis of drug-induced selection on the HIV-1 genome we demonstrate the power of the method to recover known associations and show greatly improved precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.

genetics↗