The N-terminus of murine leukaemia virus p12 protein is required for mature core stability.

The murine leukaemia virus (MLV) gag gene encodes a small protein called p12 that is essential for the early steps of viral replication. The N- and C-terminal regions of p12 are sequentially acting domains, both required for p12 function. Defects in the C-terminal domain can be overcome by introducing a chromatin binding motif into the protein. However, the function of the N-terminal domain remains unknown. Here, we undertook a detailed analysis of the effects of p12 mutation on incoming viral cores. We found that both reverse transcription complexes and isolated mature cores from N-terminal p12 mutants have altered capsid complexes compared to wild type virions. Electron microscopy revealed that mature N-terminal p12 mutant cores have different morphologies, although immature cores appear normal. Moreover, in immunofluorescent studies, both p12 and capsid proteins were lost rapidly from N-terminal p12 mutant viral cores after entry into target cells. Importantly, we determined that p12 binds directly to the MLV capsid lattice. However, we could not detect binding of an N-terminally altered p12 to capsid. Altogether, our data imply that p12 stabilises the mature MLV core, preventing premature loss of capsid, and that this is mediated by direct binding of p12 to the capsid shell. In this manner, p12 is also retained in the pre-integration complex where it facilitates tethering to mitotic chromosomes. These data also explain our previous observations that modifications to the N-terminus of p12 alter the ability of particles to abrogate restriction by TRIM5alpha and Fv1, factors that recognise viral capsid lattices.

Dynamics and restriction of murine leukemia virus cores in mitotic and interphase cells.

BACKGROUND: Murine leukemia viruses (MLVs) naturally infect unsynchronized T and B lymphocytes, thus, the incoming virus encounters both interphase and mitotic cells. While it is well accepted that MLV requires cell division to complete its replication cycle, it is not known if ab initio infection of mitotic cells can result in productive infection. This question is highly relevant since the milieu of mitotic cells is markedly different from this of interphase cells; e.g. lacking radial microtubule network and intact nuclear envelope. To follow MLV infection in mitotic and interphase cells in real-time, we employed our recently developed infectious MLV particles with labeled cores, cellular models expressing fluorescence markers of different intracellular compartments and protocols for reversible mitotic arrest of MLV-susceptible cells. RESULTS: Multi-wavelength live cell imaging was employed to simultaneously visualize GFP-labeled MLV cores, DiD-labeled viral or cellular membranes, and fluorescently-labeled microtubules or chromosomes. Cells were imaged either at interphase or upon mitotic arrest with microtubule poisons. Analysis of virus localization and trajectories revealed entry by endocytosis at interphase and mitosis, and correlation between viral mobility parameters and presence or absence of polymerized interphase microtubules. The success of infection of viruses that entered cells in mitosis was evidenced by their ability to reverse transcribe, their targeting to condensed chromosomes in the absence of radial microtubule network, and gene expression upon exit from mitosis. Comparison of infection by N, B or NB -tropic viruses in interphase and mitotic human cells revealed reduced restriction of the N-tropic virus, for infection initiated in mitosis. CONCLUSIONS: The milieu of the mitotic cells supports all necessary requirements for early stages of MLV infection. Such milieu is suboptimal for restriction of N-tropic viruses, most likely by TRIM5α.

p12 tethers the murine leukemia virus pre-integration complex to mitotic chromosomes.

The p12 protein of the murine leukemia virus (MLV) is a constituent of the pre-integration complex (PIC) but its function in this complex remains unknown. We developed an imaging system to monitor MLV PIC trafficking in live cells. This allowed the visualization of PIC docking to mitotic chromosomes and its release upon exit from mitosis. Docking occurred concomitantly with nuclear envelope breakdown and was impaired for PICs of viruses with lethal p12 mutations. Insertion of a heterologous chromatin binding module into p12 of one of these mutants restored PICs attachment to the chromosomes and partially rescued virus replication. Capsid dissociated from wild type PICs in mitotic cells but remained associated with PICs harboring tethering-negative p12 mutants. Altogether, these results explain, in part, MLV restriction to dividing cells and reveal a role for p12 as a factor that tethers MLV PIC to mitotic chromosomes.

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

BACKGROUND: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

The Gag cleavage product, p12, is a functional constituent of the murine leukemia virus pre-integration complex.

The p12 protein is a cleavage product of the Gag precursor of the murine leukemia virus (MLV). Specific mutations in p12 have been described that affect early stages of infection, rendering the virus replication-defective. Such mutants showed normal generation of genomic DNA but no formation of circular forms, which are markers of nuclear entry by the viral DNA. This suggested that p12 may function in early stages of infection but the precise mechanism of p12 action is not known. To address the function and follow the intracellular localization of the wt p12 protein, we generated tagged p12 proteins in the context of a replication-competent virus, which allowed for the detection of p12 at early stages of infection by immunofluorescence. p12 was found to be distributed to discrete puncta, indicative of macromolecular complexes. These complexes were localized to the cytoplasm early after infection, and thereafter accumulated adjacent to mitotic chromosomes. This chromosomal accumulation was impaired for p12 proteins with a mutation that rendered the virus integration-defective. Immunofluorescence demonstrated that intracellular p12 complexes co-localized with capsid, a known constituent of the MLV pre-integration complex (PIC), and immunofluorescence combined with fluorescent in situ hybridization (FISH) revealed co-localization of the p12 proteins with the incoming reverse transcribed viral DNA. Interactions of p12 with the capsid and with the viral DNA were also demonstrated by co-immunoprecipitation. These results imply that p12 proteins are components of the MLV PIC. Furthermore, a large excess of wt PICs did not rescue the defect in integration of PICs derived from mutant p12 particles, demonstrating that p12 exerts its function as part of this complex. Altogether, these results imply that p12 proteins are constituent of the MLV PIC and function in directing the PIC from the cytoplasm towards integration.

α-Aminoisobutyric acid incorporation induces cell permeability and antiviral activity of HIV-1 major homology region fragments.

The design of a cell penetrating antiviral peptide, which is derived from the major homology region of HIV-1 capsid protein and includes the non-coded α-aminoisobutyric acid, provides functional evidence for the role of the conserved region in the HIV assembly process and demonstrates the correlation between conformational stability and cellular permeability.