Biology subjects

Shroff, R.

Publications and source records attributed to Shroff, R..

6 recordsLinked to original sources

bioRxiv2025

Spatial Biology and Organoid Technologies Reveal a Potential Therapy-Resistant Cancer Stem Cell Population in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies with a 5-year survival rate of less than 10%. Chemotherapy is the current standard-of-care (SOC) for advanced PDAC; however, treatment resistance driven by a complex and immunosuppressive tumor microenvironment (TME) limits its effectiveness. To address mechanisms of resistance, we studied cabozantinib (cabo), a multi-kinase inhibitor approved for several solid tumors. Cabo has shown promise in preclinical PDAC models and clinical trials, particularly when combined with immunotherapy, but its mechanisms of action within the human PDAC TME and its potential to overcome therapy resistance remain unclear. Using CosMx Spatial Molecular Imaging (CosMx SMI) and Orion Multiplex Immunofluorescence (MxIF), we analyzed PDAC tissues collected after first line systemic chemotherapy and the Whipple surgical procedure, as well as from liver or lung metastatic sites from patients with PDAC. To functionally model the TME, we established matched patient-derived organoid (PDO) co-cultures harboring cancer associated fibroblasts (CAFs), and autologous immune cells (IMM) (PDO/CAF/IMM). These models were used to evaluate the effects of cabo and pembrolizumab, an anti-PD1 immune checkpoint inhibitor, with benchmarking of findings to the patients TME. Spatial analysis of post-chemotherapy and metastatic PDAC tissues revealed heterogeneous cellular neighborhoods within the TME, including enrichment of Schwann cells, CAFs, T regulatory cells, and cancer stem cells (CSCs). Distinct niches were observed in metastatic liver tissues, characterized by mesenchymal stem cells, fibroblasts, including myCAFs and iCAFs, and CSCs expressing CD44 and TROP2. In PDO/CAF/IMM co-cultures, treatment with cabo in combination with pembrolizumab enhanced cancer cell death by depleting myeloid-derived suppressor cells (MDSCs) and promoted cytotoxic T lymphocyte proliferation. Across both patient tissues and treated co-cultures, a persistent SOC-resistant cancer stem cell population emerged that expressed CD44 variant 9 (CD44v9). Taken together, these integrative spatial and organoid-based studies demonstrate that cabo can remodel the PDAC TME and potentiate PD-1 immunotherapy in preclinical models, while resistance associates with a CD44v9+ CSC population, revealing a potential therapeutic target.

cancer biology↗

bioRxiv2021

Deep learning redesign of PETase for practical PET degrading applications

Plastic waste poses an ecological challenge1. While current plastic waste management largely relies on unsustainable, energy-intensive, or even hazardous physicochemical and mechanical processes, enzymatic degradation offers a green and sustainable route for plastic waste recycling2. Poly(ethylene terephthalate) (PET) has been extensively used in packaging and for the manufacture of fabrics and single-used containers, accounting for 12% of global solid waste3. The practical application of PET hydrolases has been hampered by their lack of robustness and the requirement for high processing temperatures. Here, we use a structure-based, deep learning algorithm to engineer an extremely robust and highly active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives, (including a leaf-branch compost cutinase and its mutant4) and possesses enhanced thermostability and pH tolerance. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours at 50 {degrees}C. Finally, we demonstrate two paths for closed-loop PET recycling and valorization. First, we re-synthesize virgin PET from the monomers recovered after enzymatic depolymerization. Second, we enable in situ microbially-enabled valorization using a Pseudomonas strain together with FAST-PETase to degrade PET and utilize the evolved monomers as a carbon source for growth and polyhydroxyalkanoate production. Collectively, our results demonstrate the substantial improvements enabled by deep learning and a viable route for enzymatic plastic recycling at the industrial scale.

bioengineering↗

bioRxiv2021

DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins

AO_SCPLOWBSTRACTC_SCPLOWPrediction of side chain conformations of amino acids in proteins (also termed packing) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.

biophysics↗

bioRxiv2021

Multi-modal engineering of Bst DNA polymerase for thermostability in ultra-fast LAMP reactions

DNA polymerase from Geobacillus stearothermophilus, Bst DNA polymerase (Bst DNAP), is a versatile enzyme with robust strand-displacing activity that enables loop-mediated isothermal amplification (LAMP). Despite its exclusive usage in LAMP assay, its properties remain open to improvement. Here, we describe logical redesign of Bst DNAP by using multimodal application of several independent and orthogonal rational engineering methods such as domain addition, supercharging, and machine learning predictions of amino acid substitutions. The resulting Br512g3 enzyme is not only thermostable and extremely robust but it also displays improved reverse transcription activity and the ability to carry out ultrafast LAMP at 74 {degrees}C. Our study illustrates a new enzyme engineering strategy as well as contributes a novel engineered strand displacing DNA polymerase of high value to diagnostics and other fields.

bioengineering↗

bioRxiv2019

A structure-based deep learning framework for protein engineering

While deep learning methods exist to guide protein optimization, examples of novel proteins generated with these techniques require a priori mutational data. Here we report a 3D convolutional neural network that associates amino acids with neighboring chemical microenvironments at state-of-the-art accuracy. This algorithm enables identification of novel gain-of-function mutations, and subsequent experiments confirm substantive phenotypic improvements in stability-associated phenotypes in vivo across three diverse proteins.

synthetic biology↗

bioRxiv2017

Evolving Bacterial Fitness with an Expanded Genetic Code

Evolution has for the most part used the canonical 20 amino acids of the natural genetic code to construct proteins. While several theories regarding the evolution of the genetic code have been proposed, experimental exploration of these theories has largely been restricted to phylogenetic and computational modeling. The development of orthogonal translation systems has allowed noncanonical amino acids to be inserted at will into proteins. We have taken advantage of these advances to evolve bacteria to accommodate a 21 amino acid genetic code in which the amber codon ambiguously encodes either 3-nitro-L-tyrosine or stop. Such an ambiguous encoding strategy recapitulates numerous models for genetic code expansion, and we find that evolved lineages first accommodate the unnatural amino acid, and then begin to evolve on a neutral landscape where stop codons begin to appear within genes. The resultant lines represent transitional intermediates on the way to the fixation of a functional 21 amino acid code.

evolutionary biology↗