Transcription Factor Dynamics in Cross-Regulation of Plant Hormone Signaling Pathways.

Cross-regulation between hormone signaling pathways is indispensable for plant growth and development. However, the molecular mechanisms by which multiple hormones interact and co-ordinate activity need to be understood. Here, we generated a cross-regulation network explaining how hormone signals are integrated from multiple pathways in etiolated Arabidopsis (Arabidopsis thaliana) seedlings. To do so we comprehensively characterized transcription factor activity during plant hormone responses and reconstructed dynamic transcriptional regulatory models for six hormones; abscisic acid, brassinosteroid, ethylene, jasmonic acid, salicylic acid and strigolactone/karrikin. These models incorporated target data for hundreds of transcription factors and thousands of protein-protein interactions. Each hormone recruited different combinations of transcription factors, a subset of which were shared between hormones. Hub target genes existed within hormone transcriptional networks, exhibiting transcription factor activity themselves. In addition, a group of MITOGEN-ACTIVATED PROTEIN KINASES (MPKs) were identified as potential key points of cross-regulation between multiple hormones. Accordingly, the loss of function of one of these (MPK6) disrupted the global proteome, phosphoproteome and transcriptome during hormone responses. Lastly, we determined that all hormones drive substantial alternative splicing that has distinct effects on the transcriptome compared with differential gene expression, acting in early hormone responses. These results provide a comprehensive understanding of the common features of plant transcriptional regulatory pathways and how cross-regulation between hormones acts upon gene expression.

Simultaneous asymmetrical bilateral hip dislocations - A report of three cases with different presentations and management.

BACKGROUND: Simultaneous traumatic dislocation of both the hips in an individual is an uncommon injury. It accounts for only 1-2% of all traumatic hip dislocations. Simultaneous traumatic dislocation of both the hips in opposite directions i.e., anterior dislocation of one hip and posterior of the other is even rarer. These asymmetric dislocations need prompt identification for appropriate management. While their management should proceed in a manner commensurate with the accepted management of isolated dislocations to avoid complications, it is the timely identification which is essential. STUDY DETAILS: Retrospectively the details of three patients with asymmetric bilateral hip dislocations were collected. The article describes the clinical presentation of each patient and the management offered to them. The follow up duration ranged from 15 months to 24 months. RESULTS: In the short term, the outcomes in terms of hip pain, movements at the hips and radiological parameters like absence of arthritic or avascular changes were found to be satisfactory in each patient. CONCLUSION: Early diagnosis and prompt reduction is the essential first step in the appropriate management of these patients to prevent the possible complications. The associated fractures must be managed as per the established principles of trauma care. Long term follow up is essential for identifying if complications like avascular necrosis and secondary osteoarthritis develop after this uncommon injury.

Machine learning optimization of peptides for presentation by class II MHCs.

SUMMARY: T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by Major Histocompatibility Complex (MHC)-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2.

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual (≥ 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability ≤ 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

Transcriptional regulatory model of fibrosis progression in the human lung.

To develop a systems biology model of fibrosis progression within the human lung we performed RNA sequencing and microRNA analysis on 95 samples obtained from 10 idiopathic pulmonary fibrosis (IPF) and 6 control lungs. Extent of fibrosis in each sample was assessed by microCT-measured alveolar surface density (ASD) and confirmed by histology. Regulatory gene expression networks were identified using linear mixed-effect models and dynamic regulatory events miner (DREM). Differential gene expression analysis identified a core set of genes increased or decreased before fibrosis was histologically evident that continued to change with advanced fibrosis. DREM generated a systems biology model (www.sb.cs.cmu.edu/IPFReg) that identified progressively divergent gene expression tracks with microRNAs and transcription factors that specifically regulate mild or advanced fibrosis. We confirmed model predictions by demonstrating that expression of POU2AF1, previously unassociated with lung fibrosis but proposed by the model as regulator, is increased in B lymphocytes in IPF lungs and that POU2AF1-knockout mice were protected from bleomycin-induced lung fibrosis. Our results reveal distinct regulation of gene expression changes in IPF tissue that remained structurally normal compared with moderate or advanced fibrosis and suggest distinct regulatory mechanisms for each stage.

Using neural networks for reducing the dimensions of single-cell RNA-Seq data.

While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include questions about the best methods for clustering scRNA-Seq data, how to identify unique group of cells in such experiments, and how to determine the state or function of specific cells based on their expression profile. To address these issues we develop and test a method based on neural networks (NN) for the analysis and retrieval of single cell RNA-Seq data. We tested various NN architectures, some of which incorporate prior biological knowledge, and used these to obtain a reduced dimension representation of the single cell expression data. We show that the NN method improves upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of tens of thousands of single cell profiles. Such database queries (which can be performed using our web server) will enable researchers to better characterize cells when analyzing heterogeneous scRNA-Seq samples.

Innate transcriptional effects by adjuvants on the magnitude, quality, and durability of HIV envelope responses in NHPs.

Adjuvants have a critical role for improving vaccine efficacy against many pathogens, including HIV. Here, using transcriptional RNA profiling and systems serology, we assessed how distinct innate pathways altered HIV-specific antibody responses in nonhuman primates (NHPs) using 8 clinically based adjuvants. NHPs were immunized with a glycoprotein 140 HIV envelope protein (Env) and insoluble aluminum salts (alum), MF59, or adjuvant nanoemulsion (ANE) coformulated with or without Toll-like receptor 4 (TLR4) and 7 agonists. These were compared with Env administered with polyinosinic-polycytidylic acid:poly-L-lysine, carboxymethylcellulose (pIC:LC) or immune-stimulating complexes. Addition of the TLR4 agonist to alum enhanced upregulation of a set of inflammatory genes, whereas the TLR7 agonist suppressed expression of alum-responsive inflammatory genes and enhanced upregulation of antiviral and interferon (IFN) genes. Moreover, coformulation of the TLR4 or 7 agonists with alum boosted Env-binding titers approximately threefold to 10-fold compared with alum alone, but remarkably did not alter gene expression or enhance antibody titers when formulated with ANE. The hierarchy of adjuvant potency was established after the second of 4 immunizations. In terms of antibody durability, antibody titers decreased ∼10-fold after the final immunization and then remained stable after 65 weeks for all adjuvants. Last, Env-specific Fc-domain glycan structures and a series of antibody effector functions were assessed by systems serology. Antiviral/IFN gene signatures correlated with Fc-receptor binding across all adjuvant groups. This study defines the potency and durability of 8 different clinically based adjuvants in NHPs and shows how specific innate pathways can alter qualitative aspects of Env antibody function.

Transcriptome analyses identify key cellular factors associated with HIV-1-associated neuropathogenesis in infected men.

OBJECTIVE: HIV-1 viral proteins and host inflammatory factors have a direct role in neuronal toxicity in vitro; however, the contribution of these factors in vivo in HIV-1-associated neurocognitive disorder (HAND) is not fully understood. We applied novel Systems Biology approaches to identify specific cellular and viral factors and their related pathways that are associated with different stages of HAND. DESIGN: A cross-sectional study of individuals enrolled in the Multicenter AIDS Cohort Study including HIV-1-seronegative (N = 36) and HIV-1-seropositive individuals without neurocognitive symptoms (N = 16) or with mild neurocognitive disorder (MND) (N = 8) or HIV-associated dementia (HAD) (N = 16). METHODS: A systematic evaluation of global transcriptome of peripheral blood mononuclear cells (PBMCs) obtained from HIV-1-seronegative individuals and from HIV-1-positive men without neurocognitive symptoms, or MND or HAD was performed. RESULTS: MND and HAD were associated with specific changes in mRNA transcripts and microRNAs in PBMCs. Comparison of upstream regulators and TimePath analyses identified specific cellular factors associated with MND and HAD, whereas HIV-1 viral proteins played a greater role in HAD. In addition, expression of specific microRNAs - miR-let-7a, miR-124, miR-15a and others - were found to correlate with mRNA gene expression and may have a potential protective role in asymptomatic HIV-1-seropositive individuals by regulating cellular signal transduction pathways downstream of chemokines and cytokines. CONCLUSION: These results identify signature transcriptome changes in PBMCs associated with stages of HAND and shed light on the potential contribution of host cellular factors and viral proteins in HAND development.

Reconstructing the temporal progression of HIV-1 immune response pathways.

MOTIVATION: Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages. RESULTS: We present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus. TimePath uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights. We experimentally validated several of TimePaths' predictions highlighting the usefulness of temporal models. AVAILABILITY AND IMPLEMENTATION: Data, Supplementary text and the TimePath software are available from http://sb.cs.cmu.edu/timepath CONTACT: zivbj@cs.cmu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Temporal transcriptional response to latency reversing agents identifies specific factors regulating HIV-1 viral transcriptional switch.

BACKGROUND: Latent HIV-1 reservoirs are identified as one of the major challenges to achieve HIV-1 cure. Currently available strategies are associated with wide variability in outcomes both in patients and CD4(+) T cell models. This underlines the critical need to develop innovative strategies to predict and recognize ways that could result in better reactivation and eventual elimination of latent HIV-1 reservoirs. RESULTS AND DISCUSSION: In this study, we combined genome wide transcriptome datasets post activation with Systems Biology approach (Signaling and Dynamic Regulatory Events Miner, SDREM analyses) to reconstruct a dynamic signaling and regulatory network involved in reactivation mediated by specific activators using a latent cell line. This approach identified several critical regulators for each treatment, which were confirmed in follow-up validation studies using small molecule inhibitors. Results indicate that signaling pathways involving JNK and related factors as predicted by SDREM are essential for virus reactivation by suberoylanilide hydroxamic acid. ERK1/2 and NF-κB pathways have the foremost role in reactivation with prostratin and TNF-α, respectively. JAK-STAT pathway has a central role in HIV-1 transcription. Additional evaluation, using other latent J-Lat cell clones and primary T cell model, also confirmed that many of the cellular factors associated with latency reversing agents are similar, though minor differences are identified. JAK-STAT and NF-κB related pathways are critical for reversal of HIV-1 latency in primary resting T cells. CONCLUSION: These results validate our combinatorial approach to predict the regulatory cellular factors and pathways responsible for HIV-1 reactivation in latent HIV-1 harboring cell line models. JAK-STAT have a role in reversal of latency in all the HIV-1 latency models tested, including primary CD4(+) T cells, with additional cellular pathways such as NF-κB, JNK and ERK 1/2 that may have complementary role in reversal of HIV-1 latency.

Using Poly I:C as an adjuvant does not induce or exacerbate models of systemic lupus erythematosus.

Subunit vaccines are typically poorly immunogenic when administered alone, and require adjuvants for robust responses. Triggering TLRs to boost antigen-specific adaptive immunity is an attractive approach to increase the potency and quality of vaccines. However, recent reports suggest that alterations in TLR expression are associated with the pathogenesis of inflammatory and autoimmune diseases. To compare genetic studies with adjuvant studies, we examined whether stimulation through a TLR agonist induces or increases the autoimmune phenotype of healthy or autoimmune mice. C57BL/6, MRL/lpr, and Fcγr2b-deficient mice were dosed i.p. with Poly I:C every other day for 3 weeks, and monitored for signs of autoimmunity over 3 months. A separate group of mice was vaccinated three times i.m. with rPA anthrax antigen with or without Poly I:C with 2 weeks between doses. Immunized groups exhibited robust responses to vaccine and C57BL/6 and MRL/lpr mice showed a statistically significant increase in anti-rPA IgG responses in the presence of Poly I:C. Interestingly, Fcγr2b-/- mice showed increases with the base rPA vaccine, which was not significantly increased when Poly I:C was used as an adjuvant. In the chronically dosed groups, we also observed subtle alterations in levels of total antibody and some autoantibodies. However, there were no statistically significant differences in autoimmune syndrome, as measured by proteinurea, kidney pathology, weight loss, and mortality, with Poly I:C administration in chronic or vaccination mode. Taken together, these results suggest that administration of TLR3 agonists in chronic or vaccination mode does not induce or exacerbate models of systemic lupus erythematosus.

Multitask learning of signaling and regulatory networks with application to studying human response to flu.

Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

The long-term potential of biodegradable poly(lactide-co-glycolide) microparticles as the next-generation vaccine adjuvant.

Biodegradable polymeric microparticles of poly(lactide-co-glycolide) (PLG) have been extensively evaluated for drug delivery and vaccine applications over the last three decades. Despite a wealth of studies on the use of PLG microparticles in vaccines through controlled release of antigens, there is no commercial PLG-based vaccine as yet. The key challenge that prevented the development of PLG microparticles as commercial vaccines was the instability of encapsulated antigen. Over the years, advancements were made towards maintaining antigen integrity during PLG microparticle preparation and sterilization. In parallel and independently, development of PLG microparticles as therapeutic commercial products established PLG with an excellent safety record in humans, and as a suitable candidate for next-generation vaccines. Through the combination of Toll-like receptor agonist encapsulation and surface adsorption of antigen, PLG microparticles can be used as a vaccine adjuvant to address unmet medical needs, such as vaccines against HIV, malaria and TB. With strategic development of PLG-based vaccines, PLG microparticles can offer advantages over the conventional vaccine adjuvants allowing commercial development of this adjuvant.

A two-stage strategy for sterilization of poly(lactide-co-glycolide) particles by γ-irradiation does not impair their potency for vaccine delivery.

This study evaluated the feasibility of using γ-irradiation for preparing sterile poly(lactide-co-glycolide) (PLG) formulations for vaccines. PLG microparticles were prepared by water-in-oil-in-water double-emulsion technique and lyophilized. The vials were γ-irradiated for sterilization process. Antigens from Neisseria meningitidis were adsorbed onto the surface of the particles and were characterized for protein adsorption. Antigens adsorbed onto the surface of the irradiated particles within 30 min. Mice were immunized with these formulations, and vaccine potency was measured as serum bactericidal titers. The γ-irradiated PLG particles resulted in equivalent serum bactericidal titers against a panel of five N. meningitidis strains as the nonirradiated PLG particles. The use of PLG polymers with different molecular weights did not influence the vaccine potency. The PLG particles prepared by γ-irradiation of the lyophilized formulations replace the need for aseptic manufacturing of vaccine formulations. This approach may enable the use of PLG formulations with a variety of antigens and stockpiling for pandemics.

Synthesis of protein-loaded hydrogel particles in an aqueous two-phase system for coincident antigen and CpG oligonucleotide delivery to antigen-presenting cells.

Materials that effectively deliver protein antigens together with activating ligands to antigen-presenting cells are sought for improved nonviral vaccines. To this end, we synthesized protein-loaded poly(ethylene glycol) (PEG)-based hydrogel particles by cross-linking PEG within the polymer-rich phase of an emulsion formed by a poly(ethylene oxide-b-propylene oxide-b-ethylene oxide) triblock copolymer in saturated aqueous salt solution. These particles (500-nm diameter) contained high levels of encapsulated protein (approximately 75% of dry mass), which was selectively released by proteolytic enzymes normally present in the phagosomal/endosomal compartments of dendritic cells (DCs). For co-delivery of cellular activation signals, gel particles were surface-modified by sequential adsorption of poly(l-arginine) and CpG oligonucleotides. DCs pulsed with protein-loaded particles activated naïve T cells in vitro approximately 10-fold more efficiently than DCs incubated with soluble protein. This organic solvent-free strategy for protein encapsulation within submicron-sized hydrophilic particles is attractive for macromolecule delivery to a variety of phagocytic and nonphagocytic cells.

Directed cell migration via chemoattractants released from degradable microspheres.

Chemotaxis, cell migration directed by spatial concentration gradients of chemoattractant molecules, is critical for proper function of the immune system. Materials capable of generating defined chemoattractant gradients via controlled release may be useful for the design of improved vaccines and immunotherapies that draw specific cells to an immunization site. To this end, we encapsulated formyl-Nle-Leu-Phe-Nle-Tyr-Lys (fN'LFN'YK) peptides or macrophage inflammatory protein-3alpha (MIP-3alpha or CCL20) in degradable poly(lactide-co-glycolide) microspheres that provided sustained release for more than 2 weeks in vitro. fN'LFN'YK and MIP-3alpha chemoattract dendritic cells (DCs), the key antigen-presenting cells involved in generation of primary immune responses, and their precursors, monocytes. Using an in vitro videomicroscopy migration assay, we detected strong chemotaxis of human monocytes and monocyte-derived DCs through 3D collagen gels toward microspheres releasing fN'LFN'YK. Similarly, microparticles releasing MIP-3alpha were able to attract mouse bone marrow-derived dendritic cells. Strikingly, prolonged attraction of DCs from distances up to 500 microm from the source to the point of contact with individual microspheres was observed. Such microspheres could be of general interest for the design of vaccines that promote adaptive immunity and as a platform for studying the biology of chemotaxis in vitro and in vivo.

Kinetic model for designing a cancer therapy.

A kinetic model has been developed to study cancer growth. Cancer growth has been considered as interaction between various independent but interacting compartments. The model considers cell growth and metastasis resulting in the formation of new tumor masses. Using certain representative parameter values, cell growth has been modeled in the absence and the presence of various cancer therapies. Based on this analysis, the critical parameters involved in cancer development have been identified. This model may thus be useful in studying and designing a cancer therapy using the data obtained from specific in vitro experiments.

Artificial in vivo antigen presentation by the APCs and subsequent T-cell activation: a feasibility analysis.

Inappropriate antigen presentation by the antigen-presenting cells (APCs) is a cause of various diseases. One of the ways to combat these diseases is to immobilize the APCs near the infected tissue or a tissue which is susceptible to an antigen. The antigen is presented by the APCs present in the immobilized form on an implant and these upon binding to T(H)-cells result in triggering of a cascade of events as part of the natural immune response leading to the destruction of the antigen. This system has been modeled as a dialysis bag containing immobilized receptors inside the bag and the ligand diffusing out of the bag. The simulations show that by using the implant, the concentration of the ligand that has diffused into the tissue matrix can be substantially reduced and by suitably choosing the coupler size, the T(H)-cells can also effectively be activated.