Biology subjects

Karimi, M.

Publications and source records attributed to Karimi, M..

30 records · Page 2Linked to original sources

bioRxiv2020

Inhibition of ITK differentiates GVT and GVHD in allo-HSCT

Allogeneic hematopoietic stem cell transplantation is a life-saving treatment for many malignant and nonmalignant diseases. Donor T cells contained within the graft prevent tumor recurrence via graft-versus-tumor (GVT) effects, however, also cause graft-versus-host disease (GVHD). Novel treatment strategies are therefore needed to allow maintenance of GVT while suppressing GVHD. Here we show using murine models, that targeting IL-2-inducible T cell kinase (ITK) in donor T cells reduces GVHD while preserving the beneficial GVT effects. Donor T cells from Itk-/- mice exhibit significantly reduced production of inflammatory cytokines and migration to GVHD target organs such as liver and small intestine, while maintaining GVT efficacy against primary B-ALL tumors. Itk-/- T cells exhibited reduced expression of IRF4 and decreased JAK/STAT signaling activity, but preserved cytotoxicity, which was accompanied by upregulation of Eomesodermin (Eomes), which was necessary for GVT function. A novel peptide inhibitor ITK signaling is also able to prevent GVHD. This novel peptide inhibitor also reduced cytokine production in mice and human T cells. Altogether, our data suggest that inhibiting ITK could be a therapeutic strategy to reduce GVHD while preserving the beneficial GVT effects following allo-HSCT treatment. Key PointsO_LIInhibiting ITK by a novel peptide significantly reduces GVHD but retains GVT. C_LIO_LIITK deficient donor T cells exhibit minimal GVHD, but maintain GVT activity. C_LIO_LIITK deficient donor T cells exhibit significantly reduced production of inflammatory cytokines and migration to GVHD target organs. C_LIO_LIEomes is required for GVT effect. C_LI

immunology↗

bioRxiv2020

Altered N-glycan composition impacts flagella mediated adhesion in Chlamydomonas reinhardtii

For the unicellular alga Chlamydomonas reinhardtii, the presence of N-glycosylated proteins on the surface of two flagella is crucial for both cell-cell interaction during mating and flagellar surface adhesion. It is unknown whether the composition of N-glycans attached to respective proteins is important for these processes. To this end, we examined several C. reinhardtii insertional mutants and a CRIPSR/Cas9 knockout mutant of xylosyltransferase 1A, all possessing altered N-glycan compositions. Taking advantage of atomic force microscopy and micropipette force measurements, our data revealed that reduction in N-glycan complexity impedes the adhesion force required for binding the flagella to surfaces. In addition, polystyrene bead binding and transport is impaired. Notably, assembly, Intraflagellar Transport and FMG-1B transport into flagella are not affected by altered N-glycosylation. Thus, we conclude that proper N-glycosylation of flagellar proteins is crucial for adhering C. reinhardtii cells onto surfaces, indicating that N-glycans mediate surface adhesion via direct surface contact.

plant biology↗

bioRxiv2020

Network-principled deep generative models for designing drug combinations as graph sets

MotivationCombination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. ResultsWe have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed Hierarchical Variational Graph Auto-Encoders (HVGAE) trained end-to-end to jointly embed gene-gene, gene-disease, and disease-disease networks. Novel attentional pooling is introduced here for learning disease-representations from associated genes representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, HVGAE learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems-pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availabilityhttps://github.com/Shen-Lab/Drug-Combo-Generator

bioinformatics↗

bioRxiv2020

Differences in mitochondrial activity trigger cell competition during early mouse development.

Cell competition is emerging as a quality control mechanism that eliminates unfit cells in a wide range of settings from development to the adult. However, the nature of the cells normally eliminated by cell competition and what triggers their elimination remains poorly understood. In mouse, prior to gastrulation 35% of epiblast cells are eliminated. Here we have performed single cell transcriptional profiling of these cells and find that they show the hallmarks of cell competition and have mitochondrial defects. We demonstrate that mitochondrial defects are common to a range of different loser cell types and that manipulating mitochondrial function is sufficient to trigger competition. Importantly, we show that in the embryo cell competition eliminates cells with mitochondrial DNA mutations and that even non-pathological changes in mitochondrial DNA sequence can induce cell competition. Our results therefore suggest that cell competition is a purifying selection that optimises mitochondrial performance prior to gastrulation.

developmental biology↗

bioRxiv2019

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts

Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, focuses on accuracy but leaves much to be desired for interpretability. Defining inter-molecular contacts underlying affinities as a vehicle for interpretability, our large-scale interpretability assessment finds previously-used attention mechanisms inadequate. We thus formulate a hierarchical multi-objective learning problem whose predicted contacts form the basis for predicted affinities. And we solve the problem by embedding protein sequences (by hierarchical recurrent neural networks) and compound graphs (by graph neural networks) with joint attentions between protein residues and compound atoms. We further introduce three methodological advances to enhance interpretability: (1) structure-aware regularization of attentions using protein sequence-predicted solvent exposure and residue-residue contact maps; (2) supervision of attentions using known inter-molecular contacts in training data; and (3) an intrinsically explainable architecture where atomic-level contacts or "relations" lead to molecular-level affinity prediction. The first two and all three advances result in DeepAffinity+ and DeepRelations, respectively. Our methods show generalizability in affinity prediction for molecules that are new and dissimilar to training examples. Moreover, they show superior interpretability compared to state-of-the-art interpretable methods: with similar or better affinity prediction, they boost the AUPRC of contact prediction by around 33, 35, 10, and 9-fold for the default test, new-compound, new-protein, and both-new sets, respectively. We further demonstrate their potential utilities in contact-assisted docking, structure-free binding site prediction, and structure-activity relationship studies without docking. Our study represents the first model development and systematic model assessment dedicated to interpretable machine learning for structure-free compound-protein affinity prediction.

bioinformatics↗

bioRxiv2019

CrkL is required for donor T cell migration to GvHD Target organs

The success of cancer therapies based on allogeneic hematopoietic stem cell transplant relies on the ability to separate graft-versus-host disease (GvHD) from graft-versus-tumor (GVT) responses. Controlling donor T cell migration into peripheral tissues is a viable option to limit unwanted tissue damage, but a lack of specific targets limits progress on this front. Here, we show that the adaptor protein CrkL, but not the closely related family members CrkI or CrkII, is a crucial regulator of T cell migration. In vitro, CrkL-deficient T cells fail to polymerize actin in response to the integrin ligand ICAM-1, resulting in defective migration. Using a mouse model of GvHD/GVT, we found that while CrkL-deficient T cells can efficiently eliminate hematopoietic tumors they are unable to migrate into inflamed organs, such as the liver and small intestine, and thus do not cause GvHD. These results suggest a specific role for CrkL in trafficking to peripheral organs but not the lymphatic system. In line with this, we found that although CrkL-deficient T cells could clear hematopoietic tumors, they failed to clear the same tumor growing subcutaneously, highlighting the role of CrkL in controlling T cell migration into peripheral tissues. Our results define a unique role for CrkL in controlling T cell migration, and suggest that CrkL function could be therapeutically targeted to enhance the efficacy of immunotherapies involving allogeneic donor cells.

immunology↗

bioRxiv2019

De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN)

MotivationFacing data quickly accumulating on protein sequence and structure, this study is addressing the following question: to what extent could current data alone reveal deep insights into the sequence-structure relationship, such that new sequences can be designed accordingly for novel structure folds?\n\nResultsWe have developed novel deep generative models, constructed low-dimensional and generalizable representation of fold space, exploited sequence data with and without paired structures, and developed ultra-fast fold predictor as an oracle providing feedback. The resulting semi-supervised gcWGAN is assessed with the oracle over 100 novel folds not in the training set and found to generate more yields and cover 3.6 times more target folds compared to a competing data-driven method (cVAE). Assessed with structure predictor over representative novel folds (including one not even part of basis folds), gcWGAN designs are found to have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. gcWGAN explores uncharted sequence space to design proteins by learning from current sequence-structure data. The ultra fast data-driven model can be a powerful addition to principle-driven design methods through generating seed designs or tailoring sequence space.\n\nAvailabilityData and source codes will be available upon request.\n\nContactyshen@tamu.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

FACT mediates cohesin function on chromatin

Cohesin is a key regulator of genome architecture with roles in sister chromatid cohesion 1,2 and the organisation of higher-order structures during interphase 3 and mitosis 4,5. The recruitment and mobility of cohesin complexes on DNA is restricted by nucleosomes 6-8. Here we show that cohesin role in chromosome organisation requires the histone chaperone FACT. Depletion of FACT in metaphase cells affects cohesin stability on chromatin reducing its accumulation at pericentric regions and binding on chromosome arms. Using Hi-C, we show that cohesin-dependent TAD (Topological Associated Domains)-like structures in G1 and metaphase chromosomes are disrupted in the absence of FACT. Surprisingly, sister chromatid cohesion is intact in FACT-depleted cells, although chromosome segregation failure is observed. Our results uncover a role for FACT in genome organisation by facilitating cohesin-dependent compartmentalization of chromosomes into loop domains.

molecular biology↗

bioRxiv2019

Predicting Pathogenicity of Missense Variants with Weakly Supervised Regression

Quickly growing genetic variation data of unknown clinical significance demand computational methods that can reliably predict clinical phenotypes and deeply unravel molecular mechanisms. On the platform enabled by CAGI (Critical Assessment of Genome Interpretation), we develop a novel "weakly supervised" regression (WSR) model that not only predicts precise clinical significance (probability of pathogenicity) from inexact training annotations (class of pathogenicity) but also infers underlying molecular mechanisms in a variant-specific fashion. Compared to multi-class logistic regression, a representative multi-class classifier, our kernelized WSR improves the performance for the ENIGMA Challenge set from 0.72 to 0.97 in binary AUC (Area Under the receiver operating characteristic Curve) and from 0.64 to 0.80 in ordinal multi-class AUC. WSR model interpretation and protein structural interpretation reach consensus in corroborating the most probable molecular mechanisms by which some pathogenic BRCA1 variants confer clinical significance, namely metal-binding disruption for C44F and C47Y, protein-binding disruption for M18T, and structure destabilization for S1715N.

bioinformatics↗

bioRxiv2018

CRISPR-TSKO facilitates efficient cell type-, tissue-, or organ-specific mutagenesis in Arabidopsis

Detailed functional analyses of many fundamentally-important plant genes via conventional loss-of-function approaches are impeded by severe pleiotropic phenotypes. In particular, mutations in genes that are required for basic cellular functions and/or reproduction often interfere with the generation of homozygous mutant plants, precluding further functional studies. To overcome this limitation, we devised a CRISPR-based tissue-specific knockout system, CRISPR-TSKO, enabling the generation of somatic mutations in particular plant cell types, tissues, and organs. In Arabidopsis, CRISPR-TSKO mutations in essential genes caused well-defined, localized phenotypes in the root cap, stomatal lineage, or entire lateral roots. The underlying modular cloning system allows for efficient selection, identification, and functional analysis of mutant lines directly in the first transgenic generation. The efficacy of CRISPR-TSKO opens new avenues to discover and analyze gene functions in spatial and temporal contexts of plant life while avoiding pleiotropic effects of system-wide loss of gene function.

plant biology↗

bioRxiv2018

DeepAffinity: Interpretable Deep Learning of Compound Protein Affinity through Unified Recurrent and Convolutional Neural Networks

MotivationDrug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability.\n\nResultsWe present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotatedprotein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.\n\nAvailabilityData and source codes are available at https://github.com/Shen-Lab/DeepAffinity\n\nContactyshen@tamu.edu\n\nSupplementary informationSupplementary data are available at http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf.

bioinformatics↗

bioRxiv2018

The SERM/SERD Bazedoxifene Disrupts ESR1 Helix 12 to Overcome Acquired Hormone Resistance in Breast Cancer Cells

Acquired resistance to endocrine therapy remains a significant clinical burden for breast cancer patients. Somatic mutations in the ESR1 (estrogen receptor alpha (ER) gene ligand-binding domain (LBD) represent a recognized mechanism of acquired resistance. Antiestrogens with improved efficacy versus tamoxifen might overcome the resistant phenotype in ER+ breast cancers. Bazedoxifene (BZA) is a potent antiestrogen that is clinically approved for use in hormone replacement therapies. We find BZA possesses improved inhibitory potency against the Y537S and D538G ER mutants compared to tamoxifen and has additional inhibitory activity in combination with the CDK4/6 inhibitor palbociclib. In addition, comprehensive biophysical and structural biology studies show that BZAs selective estrogen receptor degrading (SERD) properties that override the stabilizing effects of the Y537S and D538G ER mutations.\n\nSignificanceBazedoxifene (BZA) is a potent orally available antiestrogen that is clinically approved for use in hormone replacement therapy (DUAVEE). We explore the efficacy of BZA to inhibit activating somatic mutants of ER that can arise in metastatic breast cancers after prolonged exposure to aromatase inhibitors or tamoxifen therapy. Breast cancer cell line, biophysical, and structural data show that BZA disrupts helix 12 of the ER ligand binding domain to achieve improved potency against Y537S and D538G somatic mutants compared to 4-hydroxytamoxifen.

cancer biology↗