Universal open MHC-I molecules for rapid peptide loading and enhanced complex stability across HLA allotypes.

The polymorphic nature and intrinsic instability of class I major histocompatibility complex (MHC-I) and MHC-like molecules loaded with suboptimal peptides, metabolites, or glycolipids presents a fundamental challenge for identifying disease-relevant antigens and antigen-specific T cell receptors (TCRs), hindering the development of autologous therapeutics. Here, we leverage the positive allosteric coupling between the peptide and light chain (ß2 microglobulin, ß2m) subunits for binding to the MHC-I heavy chain (HC) through an engineered disulfide bond bridging conserved epitopes across the HC/ß2m interface, to generate conformationally stable, peptide-receptive molecules named "open MHC-I." Biophysical characterization shows that open MHC-I molecules are properly folded protein complexes of enhanced thermal stability compared to the wild type when loaded with low- to moderate-affinity peptides. Using solution NMR, we characterize the effects of the disulfide bond on the conformation and dynamics of the MHC-I structure, ranging from local changes in ß2m-interacting sites of the peptide-binding groove to long-range effects on the α2-1 helix and α3 domain. The interchain disulfide bond stabilizes MHC-I molecules in an open conformation to promote peptide exchange across multiple human leukocyte antigen (HLA) allotypes, covering representatives from five HLA-A supertypes, six HLA-B supertypes, and oligomorphic HLA-Ib molecules. Our structure-guided design, combined with conditional ß-peptide ligands, provides a universal platform to generate ready-to-load MHC-I systems of enhanced stability, enabling a range of approaches to screen antigenic epitope libraries and probe polyclonal TCR repertoires covering highly polymorphic HLA-I allotypes, as well as oligomorphic nonclassical molecules.

miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes.

Discovering pre-microRNAs (miRNAs) is the core of miRNA discovery. Using traditional sequence/structural features, many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performance has been very low. This becomes more grave in plants where unlike animals pre-miRNAs are much more complex and difficult to identify. A huge gap exists between animals and plants for the available software for miRNA discovery and species-specific miRNA information. Here, we present miWords, a composite deep learning system of transformers and convolutional neural networks which sees genome as a pool of sentences made of words with specific occurrence preferences and contexts, to accurately identify pre-miRNA regions across plant genomes. A comprehensive benchmarking was done involving >10 software representing different genre and many experimentally validated datasets. miWords emerged as the best one while breaching accuracy of 98% and performance lead of ~10%. miWords was also evaluated across Arabidopsis genome where also it outperformed the compared tools. As a demonstration, miWords was run across the tea genome, reporting 803 pre-miRNA regions, all validated by small RNA-seq reads from multiple samples, and most of them were functionally supported by the degradome sequencing data. miWords is freely available as stand-alone source codes at https://scbb.ihbt.res.in/miWords/index.php.

DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery.

We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During multiple benchmarkings DeepPlnc consistently outperformed all the compared tools and maintained a highly significant lead in the range of 2.5%- 4.6% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.

Variant analyses of candidate genes in orofacial clefts in multi-ethnic populations.

OBJECTIVES: Cleft lip with/without cleft palate and cleft palate only is congenital birth defects where the upper lip and/or palate fail to fuse properly during embryonic facial development. Affecting ~1.2/1000 live births worldwide, these orofacial clefts impose significant social and financial burdens on affected individuals and their families. Orofacial clefts have a complex etiology resulting from genetic variants combined with environmental covariates. Recent genome-wide association studies and whole-exome sequencing for orofacial clefts identified significant genetic associations and variants in several genes. Of these, we investigated the role of common/rare variants in SHH, RORA, MRPL53, ACVR1, and GDF11. MATERIALS AND METHODS: We sequenced these five genes in 1255 multi-ethnic cleft lip with/without palate and cleft palate only samples in order to find variants that may provide potential explanations for the missing heritability of orofacial clefts. Rare and novel variants were further analyzed using in silico predictive tools. RESULTS: Ninteen total variants of interest were found, with variant types including stop-gain, missense, synonymous, intronic, and splice-site variants. Of these, 3 novel missense variants were found, one in SHH, one in RORA, and one in GDF11. CONCLUSION: This study provides evidence that variants in SHH, RORA, MRPL53, ACVR1, and GDF11 may contribute to risk of orofacial clefts in various populations.

A structure-guided approach to predict MHC-I restriction of T cell receptors for public antigens.

Peptides presented by class I major histocompatibility complex (MHC-I) proteins provide biomarkers for therapeutic targeting using T cell receptors (TCRs), TCR-mimicking antibodies (TMAs), or other engineered protein binders. Despite the extreme sequence diversity of the Human Leucocyte Antigen (HLA, the human MHC), a given TCR or TMA is restricted to recognize epitopic peptides in the context of a limited set of different HLA allotypes. Here, guided by our analysis of 96 TCR:pHLA complex structures in the Protein Data Bank (PDB), we identify TCR contact residues and classify 148 common HLA allotypes into T-cell cross-reactivity groups (T-CREGs) on the basis of their interaction surface features. Insights from our work have actionable value for resolving MHC-I restriction of TCRs, guiding therapeutic expansion of existing therapies, and informing the selection of peptide targets for forthcoming immunotherapy modalities.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within a curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these representative backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer peptide/HLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in terms of structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work provide a framework for linking conformational diversity with antigen immunogenicity and receptor cross-reactivity.

RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features.

RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.

Universal open MHC-I molecules for rapid peptide loading and enhanced complex stability across HLA allotypes.

The polymorphic nature and intrinsic instability of class I major histocompatibility complex (MHC-I) and MHC-like molecules loaded with suboptimal peptides, metabolites, or glycolipids presents a fundamental challenge for identifying disease-relevant antigens and antigen-specific T cell receptors (TCRs), hindering the development of autologous therapeutics. Here, we leverage the positive allosteric coupling between the peptide and light chain (ß 2 microglobulin, ß 2 m) subunits for binding to the MHC-I heavy chain (HC) through an engineered disulfide bond bridging conserved epitopes across the HC/ß 2 m interface, to generate conformationally stable, open MHC-I molecules. Biophysical characterization shows that open MHC-I molecules are properly folded protein complexes of enhanced thermal stability compared to the wild type, when loaded with low- to intermediate-affinity peptides. Using solution NMR, we characterize the effects of the disulfide bond on the conformation and dynamics of the MHC-I structure, ranging from local changes in ß 2 m interacting sites of the peptide binding groove to long-range effects on the α 2-1 helix and α 3 domain. The interchain disulfide bond stabilizes empty MHC-I molecules in a peptide-receptive, open conformation to promote peptide exchange across multiple human leucocyte antigen (HLA) allotypes, covering representatives from five HLA-A, six HLA-B supertypes, and oligomorphic HLA-Ib molecules. Our structural design, combined with conditional ß-peptide ligands, provides a universal platform for generating ready-to-load MHC-I systems of enhanced stability, enabling a range of approaches to screen antigenic epitope libraries and probe polyclonal TCR repertoires in the context of highly polymorphic HLA-I allotypes, as well as oligomorphic nonclassical molecules. Significance Statement: We outline a structure-guided approach for generating conformationally stable, open MHC-I molecules with enhanced ligand exchange kinetics spanning five HLA-A, all HLA-B supertypes, and oligomorphic HLA-Ib allotypes. We present direct evidence of positive allosteric cooperativity between peptide binding and ß 2 m association with the heavy chain by solution NMR and HDX-MS spectroscopy. We demonstrate that covalently linked ß 2 m serves as a conformational chaperone to stabilize empty MHC-I molecules in a peptide-receptive state, by inducing an open conformation and preventing intrinsically unstable heterodimers from irreversible aggregation. Our study provides structural and biophysical insights into the conformational properties of MHC-I ternary complexes, which can be further applied to improve the design of ultra-stable, universal ligand exchange systems in a pan-HLA allelic setting.

Role of Alternative Elicitor Transporters in the Onset of Plant Host Colonization by Streptomyces scabiei 87-22.

Plant colonization by Streptomyces scabiei, the main cause of common scab disease on root and tuber crops, is triggered by cello-oligosaccharides, cellotriose being the most efficient elicitor. The import of cello-oligosaccharides via the ATP-binding cassette (ABC) transporter CebEFG-MsiK induces the production of thaxtomin phytotoxins, the central virulence determinants of this species, as well as many other metabolites that compose the 'virulome' of S. scabiei. Homology searches revealed paralogues of the CebEFG proteins, encoded by the cebEFG2 cluster, while another ABC-type transporter, PitEFG, is encoded on the pathogenicity island (PAI). We investigated the gene expression of these candidate alternative elicitor importers in S. scabiei 87-22 upon cello-oligosaccharide supply by transcriptomic analysis, which revealed that cebEFG2 expression is highly activated by both cellobiose and cellotriose, while pitEFG expression was barely induced. Accordingly, deletion of pitE had no impact on virulence and thaxtomin production under the conditions tested, while the deletion of cebEFG2 reduced virulence and thaxtomin production, though not as strong as the mutants of the main cello-oligosaccharide transporter cebEFG1. Our results thus suggest that both ceb clusters participate, at different levels, in importing the virulence elicitors, while PitEFG plays no role in this process under the conditions tested. Interestingly, under more complex culture conditions, the addition of cellobiose restored thaxtomin production when both ceb clusters were disabled, suggesting the existence of an additional mechanism that is involved in sensing or importing the elicitor of the onset of the pathogenic lifestyle of S. scabiei.

Structural principles of peptide-centric chimeric antigen receptor recognition guide therapeutic expansion.

Peptide-centric chimeric antigen receptors (PC-CARs) recognize oncoprotein epitopes displayed by cell-surface human leukocyte antigens (HLAs) and offer a promising strategy for targeted cancer therapy. We have previously developed a PC-CAR targeting a neuroblastoma-associated PHOX2B peptide, leading to robust tumor cell lysis restricted by two common HLA allotypes. Here, we determine the 2.1-angstrom crystal structure of the PC-CAR-PHOX2B-HLA-A*24:02-ß2m complex, which reveals the basis for antigen-specific recognition through interactions with CAR complementarity-determining regions (CDRs). This PC-CAR adopts a diagonal docking mode, where interactions with both conserved and polymorphic HLA framework residues permit recognition of multiple HLA allotypes from the A9 serological cross-reactive group, covering a combined global population frequency of up to 46.7%. Biochemical binding assays, molecular dynamics simulations, and structural and functional analyses demonstrate that high-affinity PC-CAR recognition of cross-reactive pHLAs necessitates the presentation of a specific peptide backbone, where subtle structural adaptations of the peptide are critical for high-affinity complex formation, and CAR T cell killing. Our results provide a molecular blueprint for engineering CARs with optimal recognition of tumor-associated antigens in the context of different HLAs, while minimizing cross-reactivity with self-epitopes.

Structural principles of peptide-centric Chimeric Antigen Receptor recognition guide therapeutic expansion.

Peptide-Centric Chimeric Antigen Receptors (PC-CARs), which recognize oncoprotein epitopes displayed by human leukocyte antigens (HLAs) on the cell surface, offer a promising strategy for targeted cancer therapy 1 . We have previously developed a PC-CAR targeting a neuroblastoma- associated PHOX2B peptide, leading to robust tumor cell lysis restricted by two common HLA allotypes 2 . Here, we determine the 2.1 Å structure of the PC-CAR:PHOX2B/HLA-A*24:02/ß2m complex, which reveals the basis for antigen-specific recognition through interactions with CAR complementarity-determining regions (CDRs). The PC-CAR adopts a diagonal docking mode, where interactions with both conserved and polymorphic HLA framework residues permit recognition of multiple HLA allotypes from the A9 serological cross-reactivity group, covering a combined American population frequency of up to 25.2%. Comprehensive characterization using biochemical binding assays, molecular dynamics simulations, and structural and functional analyses demonstrate that high-affinity PC-CAR recognition of cross-reactive pHLAs necessitates the presentation of a specific peptide backbone, where subtle structural adaptations of the peptide are critical for high-affinity complex formation and CAR-T cell killing. Our results provide a molecular blueprint for engineering CARs with optimal recognition of tumor-associated antigens in the context of different HLAs, while minimizing cross-reactivity with self-epitopes.

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery.

Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.

Detection of melanoma cells in vitro using an optical detector of photoacoustic waves.

BACKGROUND AND OBJECTIVE: Circulating tumor cells have been shown to correlate positively with metastatic disease state in patients with advanced cancer. We have demonstrated the ability to detect melanoma cells in a flow system by generating and detecting photoacoustic waves in melanoma cells. This method is similar to flow cytometry, although using photoacoustics rather than fluorescence. Previously, we used piezoelectric films as our acoustic sensors. However, such films have indicated false-positive signals due to unwanted direct interactions between photons from the high laser fluence in the flow system and the film itself. We have adapted an optical detection scheme that obviates the need for piezoelectric films. STUDY DESIGN/MATERIALS AND METHODS: Our photoacoustic system comprised a tunable laser system with an output of 410-710 nm with a pulse duration of 5 nanoseconds. The light was delivered by optical fiber to a glass microcuvette that contained saline buffer suspensions of melanoma and white blood cells. We used a continuous HeNe laser to provide a probe beam that reflected off of a glass and water interface in close proximity to the microcuvette. The beam was detected by a high-speed photodiode. When a photoacoustic wave was generated in the microcuvette, the wave propagated and changed the reflectance of the beam due to index of refraction change in the water. This perturbation was used to detect the presence of melanoma cells. RESULTS: We determined a detection threshold of about one individual melanoma cell with no pyroelectric noise indicated in the signals. CONCLUSIONS: The optical reflectance method provides sensitivity to detect small numbers of melanoma cells without created false-positive signals from pyroelectric interference, showing promise as a means to perform tests for circulating melanoma cells in blood samples.

Correction to: Retransplantation after post transplant lymphoproliferative disorder: overcoming the obstacles!

In the original publication of the article, while submitting the case report.

Retransplantation after post transplant lymphoproliferative disorder: overcoming the obstacles!

Post transplant lymphoproliferative disorder (PTLD) is a rare complication after kidney transplantation. Graft dysfunction is often encountered during the course of the treatment of PTLD, at times leading to need for retransplantation. We describe here the case of a young boy who underwent retransplantation after treatment of early Epstein Barr virus (EBV) related post transplant lymphoproliferative disorder. Our case highlights the various factors needing deliberation before retransplantation including time from remission of PTLD, EBV serostatus and choice of induction and maintenance immunosuppression agents.

Photothermal therapy mediated by gum Arabic-conjugated gold nanoparticles suppresses liver preneoplastic lesions in mice.

This study validates the utility of Gum Arabic-conjugated gold nanoparticles (GA-AuNPs) and laser to induce photothermal inhibition of hepatocarcinogenesis, via employing a diethylnitrosamine (DEN)-mediated hepatocellular carcinoma model. This work included both of in vitro and in vivo studies; to investigate the GA-AuNPs cytotoxicity and phototoxicity in hepatic cell line; to delineate the GA-AuNPs therapeutic efficiency in DEN-induced preneoplastic lesions (PNLs) in the liver of Balb-C mice. The therapeutic effects of GA-AuNPs on the mediators of apoptosis, inflammation, and tumor initiation, as well as the histopathological changes in preneoplastic liver have been investigated. Our results infer that GA-AuNPs in combination with laser irradiation led to a significant reduction in the cell viability and in histone deacetylase activity in hepatocarcinoma HepG2 cells. In chemically-induced PNLs mice model our results have demonstrated that GA-AuNPs, with or without laser irradiation, induced cancer cell apoptosis through the activation of death receptors DR5 and caspase-3 and inhibited both of the PNLs incidence and the initiation marker (placental glutathione S-transferase; GST-P). The laser-stimulated GA-AuNPs significantly reduced the tumor necrosis factor-α levels. In summary, GA-AuNPs with laser treatment inhibited liver PNLs via the induction of the extrinsic apoptosis pathway and the inhibition of inflammation.

Levamisole/Cocaine Induced Systemic Vasculitis and Immune Complex Glomerulonephritis.

Levamisole is an antihelminthic and immunomodulator medication that was banned by the USFDA in 1998. It has been increasingly used to adulterate cocaine due to its psychotropic effects and morphological properties. Adverse reactions including cutaneous vasculitis, thrombocytopenia, and agranulocytosis have been well described. Despite systemic vasculitis in this setting, renal involvement is uncommon. We report here a case of ANCA positive systemic vasculitis with biopsy proven immune complex mediated glomerulonephritis likely secondary to levamisole/cocaine. A 40-year-old Caucasian male with no past medical history presented with 3-week history of fatigue, skin rash, joint pains, painful oral lesions, oliguria, hematuria, worsening dyspnea on exertion, and progressive lower extremity edema. He had a history of regular tobacco and cocaine use. Lab testing revealed severe anemia, marked azotemia, deranged electrolytes, and 4.7 gm proteinuria. Rheumatologic testing revealed hypocomplementemia, borderline ANA, myeloperoxidase antibody, and positive atypical p-ANCA. Infectious and other autoimmune workup was negative. Kidney biopsy was consistent with immune mediated glomerulonephritis and showed mesangial proliferation and immune complex deposition consisting of IgG, IgM, and complement. High dose corticosteroids and discontinuing cocaine use resulted in marked improvement in rash, mucocutaneous lesions, and arthritis. There was no renal recovery and he remained hemodialysis dependent.