Sequence Similarity Research Articles

Microbulbifer magnicolonia sp. nov., isolated from sediment of tidal flat located in Zhoushan, China.

A Gram-stain-negative, aerobic, motile with flagella and rod- or ovoid-shaped bacterium, designated GG15T, was isolated from tidal flat sediment sampled in Zhoushan, Zhejiang Province. Strain GG15T grew at 20-40 °C (optimum, 30 °C), at pH 5.5-9.5 (optimum, pH 7.0-8.0) and with 1.0-10.0 % (w/v) NaCl (optimum, 1.5 %). Colony diameters ranged from 1 to 3 mm within the first week, reaching a maximum of 6-7 mm after 15 days of cultivation. Strain GG15T exhibited highest 16S rRNA gene sequence similarity to Microbulbifer taiwanensis CCM 7856T (98.1 %), with similarity to other species within the genus Microbulbifer ranging from 97.8 to 93.8 %. Similarity values to other genera were below 93.8 %. Strain GG15T exhibited positive activity for β-glucosidase, trypsin and chymotrypsin, whereas the reference strain showed negative activity. Chemotaxonomic analyses indicated that strain GG15T contained Q-8 as the sole respiratory quinone, C16 : 0 (9.1 %), iso-C15 : 0 (30.9 %) and iso-C11 : 0 3-OH (7.2 %) as the predominant fatty acids, and phosphatidylethanolamine, phosphatidylglycerol, three unidentified lipids, four unidentified glycolipids, one unidentified phospholipid, two unidentified aminolipids and two unidentified aminophospholipids as the main polar lipids. The genome of strain GG15T was 4 307 641 bp long, comprising 3861 protein-coding genes. The G+C content of strain GG15T was 61.5 mol% based on its genomic sequence. Strain GG15T showed low digital DNA-DNA hybridization (<70 %) and average nucleotide identity values (<95 %) with other Microbulbifer species. As a result, a novel species within the genus Microbulbifer, named Microbulbifer magnicolonia sp. nov., is proposed. The type strain is GG15T (MCCC 1K08802T=KCTC 8210T).

Keguizhuia sedimenti gen. nov., sp. nov., isolated from river sediment represents a novel genus of the family Oxalobacteraceae.

A Gram-stain-negative bacterium, designated as R-40T, was isolated from sediment of the Mulong river in Mianyang city, Sichuan province, PR China. The cells of strain R-40T were aerobic non-motile and formed translucent white colonies on R2A agar. Growth occurred at 15-37 °C (optimum 30 °C), pH 5.0-9.0 (optimum 7.0) and salinities of 0-3.0 % (w/v, optimum 0 %). R-40T showed 95.2-96.6 % 16S rRNA gene sequence similarities with the type strains of species of the genera Oxalicibacterium, Herminiimonas, Lacisediminimonas, Paucimonas, Herbaspirillum and Noviherbaspirillum in the family Oxalobacteraceae. The results of phylogenetic analysis based on genome sequences indicated that the strain was clustered with type strains of species of the genera Oxalicibacterium and Herminiimonas in the family Oxalobacteraceae but formed a distinct lineage. The average nucleotide identity (ANI), digital DNA-DNA hybridization (dDDH) and average amino acid identity (AAI) values between R-40T and type strains of species of the genera Oxalicibacterium, Herminiimonas, Lacisediminimonas, Paucimonas, Herbaspirillum and Noviherbaspirillum ranged from 69.3 to 74.1 %, from 18.2 to 21.4 % and from 60.1 to 67.4 %, respectively. The major cellular fatty acids were C16 : 0, C17 : 0 cyclo and summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c). The major quinone was ubiquinone-8 (Q-8). The polar lipid profile consisted of diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phospholipid and small amounts of glycophospholipids. The genome size of R-40T was 5.1 Mbp with 54.0 % DNA G+C content. On the basis of the evidence presented in this study, strain R-40T represents a novel species of a novel genus in the family Oxalobacteraceae, for which the name Keguizhuia sedimenti gen. nov., sp. nov. (type strain R-40T=MCCC 1K08818T=KCTC 8137T) is proposed.

Scoring alignments by embedding vector similarity.

Sequence similarity is of paramount importance in biology, as similar sequences tend to have similar function and share common ancestry. Scoring matrices, such as PAM or BLOSUM, play a crucial role in all bioinformatics algorithms for identifying similarities, but have the drawback that they are fixed, independent of context. We propose a new scoring method for amino acid similarity that remedies this weakness, being contextually dependent. It relies on recent advances in deep learning architectures that employ self-supervised learning in order to leverage the power of enormous amounts of unlabelled data to generate contextual embeddings, which are vector representations for words. These ideas have been applied to protein sequences, producing embedding vectors for protein residues. We propose the E-score between two residues as the cosine similarity between their embedding vector representations. Thorough testing on a wide variety of reference multiple sequence alignments indicate that the alignments produced using the new $E$-score method, especially ProtT5-score, are significantly better than those obtained using BLOSUM matrices. The new method proposes to change the way alignments are computed, with far-reaching implications in all areas of textual data that use sequence similarity. The program to compute alignments based on various $E$-scores is available as a web server at e-score.csd.uwo.ca. The source code is freely available for download from github.com/lucian-ilie/E-score.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.

Rathayibacter tanaceti sp. nov., a Novel Actinobacterium from Tanacetum vulgare Infested by Foliar Nematode Aphelenchoides sp.

Two novel yellow-pigmented, rod-shaped and non-motile coryneform actinobacteria, strains VKM Ac-2596T and VKM Ac-2761, were isolated from a plant Tanacetum vulgare (Asteraceae) infested by foliar nematode Aphelenchoides sp. The strains exhibited the highest 16S rRNA gene sequence similarities to Rathayibacter agropyri CA4T (99.71%), Rathayibacter rathayi DSM 7485T (99.65%) and Rathayibacter iranicus VKM Ac-1602T (99.65%). The pairwise average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between VKM Ac-2596T and VKM Ac-2671 towards the type strains of Rathayibacter species did not exceed 85.24% and 29.40%, respectively, that are well below the thresholds for species delineation. The target strains had key chemotaxonomic properties typical of the genus Rathayibacter, namely, the DAB-based peptidoglycan, rhamnose and mannose as the predominant sugars and a rhamnomannan in the cell, the major menaquinone MK-10 and fatty acids of iso-anteiso type, with a large proportion of anteiso-15:0. The strains showed clear differences from the recognized Rathayibacter species in several phenotypic characteristics, including the difference in the composition of cell wall glycopolymers. Based on the results obtained in this study and the data published previously, we provide a description of a new species, Rathayibacter tanaceti sp. nov., with DL-642T (= VKM Ac-2596T = LMG 33114T) as the type strain.

Substrate specificity mapping of fungal CAZy AA3_2 oxidoreductases

BackgroundOxidative enzymes targeting lignocellulosic substrates are presently classified into various auxiliary activity (AA) families within the carbohydrate-active enzyme (CAZy) database. Among these, the fungal AA3 glucose–methanol–choline (GMC) oxidoreductases with varying auxiliary activities are attractive sustainable biocatalysts and important for biological function. CAZy AA3 enzymes are further subdivided into four subfamilies, with the large AA3_2 subfamily displaying diverse substrate specificities. However, limited numbers of enzymes in the AA3_2 subfamily are currently biochemically characterized, which limits the homology-based mining of new AA3_2 oxidoreductases. Importantly, novel enzyme activities may be discovered from the uncharacterized parts of this large subfamily.ResultsIn this study, phylogenetic analyses employing a sequence similarity network (SSN) and maximum likelihood trees were used to cluster AA3_2 sequences. A total of 27 AA3_2 proteins representing different clusters were selected for recombinant production. Among them, seven new AA3_2 oxidoreductases were successfully produced, purified, and characterized. These enzymes included two glucose dehydrogenases (TaGdhA and McGdhA), one glucose oxidase (ApGoxA), one aryl alcohol oxidase (PsAaoA), two aryl alcohol dehydrogenases (AsAadhA and AsAadhB), and one novel oligosaccharide (gentiobiose) dehydrogenase (KiOdhA). Notably, two dehydrogenases (TaGdhA and KiOdhA) were found with the ability to utilize phenoxy radicals as an electron acceptor. Interestingly, phenoxy radicals were found to compete with molecular oxygen in aerobic environments when serving as an electron acceptor for two oxidases (ApGoxA and PsAaoA), which sheds light on their versatility. Furthermore, the molecular determinants governing their diverse enzymatic functions were discussed based on the homology model generated by AlphaFold.ConclusionsThe phylogenetic analyses and biochemical characterization of AA3_2s provide valuable guidance for future investigation of AA3_2 sequences and proteins. A clear correlation between enzymatic function and SSN clustering was observed. The discovery and biochemical characterization of these new AA3_2 oxidoreductases brings exciting prospects for biotechnological applications and broadens our understanding of their biological functions.

Knocking out FAM20C in pre-osteoblasts leads to up-regulation of osteoclast differentiation to affect long bone development

Family with sequence similarity 20 member C (FAM20C) is a Golgi casein kinase that phosphorylates extracellularly-secreted regulatory proteins involved in bone development and mineralization, but its specific role in bone development is still largely unknown. In this study, to examine the specific mechanisms that FAM20C influences bone development, we cross-bred Osx-Cre with FAM20Cflox/flox mice to establish a Osx-Cre; FAM20Cflox/flox knockout (oKO) mouse model; FAM20C was KO in pre-osteoblasts. oKO development was examined at 1–10 weeks, in which compared to control FAM20Cflox/flox, they had lower body weights and bone tissue mineralization. Furthermore, oKO had lower bone volume fractions, thickness, and trabecular numbers, along with higher degrees of trabecular separation. These mice also had decreased femoral metaphyseal cartilage proliferation layer, along with thickened hypertrophic layer and increased apoptotic cell counts. Transcriptomic analysis found that differentially-expressed genes in oKO were concentrated in the osteoclast differentiation pathway, in line with increased osteoclast presence. Additionally, up-regulation of osteoclast-related, and down-regulation of osteogenesis-related genes, were identified, in which the most up-regulated genes were signal regulatory protein β-1 family (Sirpb1a-c) and mitogen-activated protein kinase 13. Overall, FAM20C KO in pre-osteoblasts leads to abnormal long bone development, likely due to subsequent up-regulation of osteoclast differentiation-associated genes.

Microbacterium abyssi sp. nov. and Microbacterium limosum sp. nov., two new species of the genus Microbacterium, isolated from deep-sea sediment samples.

Two Gram-positive, non-motile, short rod-shaped actinomycete strains, designated as A18JL241T and Y20T, were isolated from deep-sea sediment samples collected from the Southwest Indian Ocean and Western Pacific Ocean, respectively. Both of the isolates were able to grow within the temperature range of 5-40 °C, NaCl concentration range of 0-7 % (w/v) and at pH 6.0-12.0. The two most abundant cellular fatty acids of both strains were anteiso-C15 : 0 and anteiso-C17 : 0. The major polar lipid contents of the two strains were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine and one unidentified glycolipid. These two strains shared common chemotaxonomic features comprising MK-10 and MK-12 as the respiratory quinones. The genomic DNA G+C contents of the two strains were 68.1 and 70.4 mol%, respectively. The 16S rRNA gene phylogeny showed that the novel strains formed two distinct sublines within the genus Microbacterium. Strain A18JL241T was most closely related to the type strain of Microbacterium tenebrionis KCTC 49593T (98.8 % sequence similarity), whereas strain Y20T formed a tight cluster with the type strain of Microbacterium schleiferi NBRC 15075T (99.0 %). The orthologous average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values with the type strains of related Microbacterium species were in the range of 74.1-89.1 % and 19.4-36.9 %, respectively, which were below the recognized thresholds of 95-96 % ANI and 70 % dDDH for species definition. Based on the results obtained here, it can be concluded that strains A18JL241T and Y20T represent two novel species of the genus Microbacterium, for which the names Microbacterium abyssi sp. nov. (type strain A18JL241T=JCM 33956T=MCCC 1A16622T) and Microbacterium limosum sp. nov. (type strain Y20T=JCM 33960T=MCCC 1A16747T) are proposed.

Levenshtein Distance Embedding with Poisson Regression for DNA Storage

Efficient computation or approximation of Levenshtein distance, a widely-used metric for evaluating sequence similarity, has attracted significant attention with the emergence of DNA storage and other biological applications. Sequence embedding, which maps Levenshtein distance to a conventional distance between embedding vectors, has emerged as a promising solution. In this paper, a novel neural network-based sequence embedding technique using Poisson regression is proposed. We first provide a theoretical analysis of the impact of embedding dimension on model performance and present a criterion for selecting an appropriate embedding dimension. Under this embedding dimension, the Poisson regression is introduced by assuming the Levenshtein distance between sequences of fixed length following a Poisson distribution, which naturally aligns with the definition of Levenshtein distance. Moreover, from the perspective of the distribution of embedding distances, Poisson regression approximates the negative log likelihood of the chi-squared distribution and offers advancements in removing the skewness. Through comprehensive experiments on real DNA storage data, we demonstrate the superior performance of the proposed method compared to state-of-the-art approaches.

SAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

Abstract 1236: Characterization of CBX-250, a first-in-class TCR-mimetic-based T-cell engager targeting a cathepsin G peptide-HLA complex for the treatment of myeloid leukemia

Abstract While T-cell engaging therapies have demonstrated clear clinical benefit in the treatment of B-cell malignancies, their application to myeloid malignancies remains a challenge due to the limited repertoire of suitable tumor specific surface antigens. Targeting HLA-restricted peptides (pHLA) derived from intracellular cancer antigens provides an opportunity to explore the entire cancer proteome. CG1 (FLLPTGAEA) has been validated as an HLA-A*02:01 restricted leader peptide from Cathepsin G (CTSG) and is abundantly presented by leukemic versus normal myeloid cells. TCR-mimetic (TCRm) antibodies are ideally suited to target pHLA with high potency and specificity and can be engineered and manufactured using standard antibody technologies. Here we report the preclinical characterization of CBX-250, a novel CG1/HLA-A2 TCRm-CD3 bispecific T-Cell Engager (TCE). CBX-250 induced potent T-cell activation and T-cell mediated killing of leukemia cell lines with varying levels of target antigen density with sub-nM EC50 in vitro. In vivo, a closely related precursor of CBX-250 demonstrated potent tumor control in various AML and CML CDX models at doses as low as 0.01mpk. Although CTSG is a serine protease stored in neutrophil azurophilic granules, the CG1 peptide is preferentially presented by leukemia cells, as validated by mass spectrometry. Moreover, when co-culturing PBMCs with HLA-A2 neutrophils, we did not observe any CBX-250-mediated T-cell activation or IFNγ production. In silico predictions identified HLA-A2-restricted human peptides that share sequence or structural similarity to CG1. We determined the cross-reactivity risk of CBX-250 to be low, based on T-cell activation and T-cell mediated cytotoxicity assays against cells pulsed with supra-physiological concentrations of these peptides. Moreover, we observed no activity against target-negative cancer cells, including a CTSG-KO cell line. To further assess the specificity of CBX-250, we screened a panel of normal cells and found no meaningful T-cell activation or IFNγ production at CBX-250 concentrations exceeding its EC90. Finally, the CBX-250 precursor molecule demonstrated favorable safety in a double transgenic mouse model expressing human HLA-A2 and CG1. Following sequence optimization, CBX-250 demonstrated robust serum and pH stress stability and a favorable melting temperature, while retaining excellent potency and specificity. A full suite of analytical and biophysical assessments supports CBX-250’s favorable developability profile. In summary, these data provide strong preclinical evidence of the potency, specificity, safety, and developability of CBX-250, a novel, first-in-class, off-the-shelf, TCRm-based TCE for the treatment of myeloid malignancies. Citation Format: Geraldine Paulus, Benjamin Lee, Preethi Sankaran, Tanzila Rahman, Jessica Jimenez, Delainey O'Connor, Simon Yue, Yu Huang, Melissa Bikowitz, Sarah Jaffe, Shawn O'Malley, Bhupal Ban, Galina Gabriely, Tao Wang, Amanda Mak, Michael Princiotta, Chunhua Shi, Helen He, Ningping Feng, Jun Yan, Timothy Heffernan, Gheath Alatrash, Jeffrey Molldrem, Dmitri Wiederschain. Characterization of CBX-250, a first-in-class TCR-mimetic-based T-cell engager targeting a cathepsin G peptide-HLA complex for the treatment of myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1236.

Abstract 1169: Increased immunotherapy sensitivity associated with damaging IFN-gamma pathway mutations in NSCLC

Abstract Introduction: In vivo CRISPR screens across multiple murine models of malignancy revealed that loss of interferon gamma (IFN-γ)-mediated sensing predisposes to immune elimination. We assessed the association between damaging mutations in the IFN-γ pathway and clinical outcomes after immune checkpoint inhibition in the Stand Up to Cancer-Mark Foundation non-small cell lung cancer (NSCLC) cohort. Methods: The sensitizing IFN-γ pathway genes were defined as IFNGR1, IFNGR2, JAK1, JAK2, STAT1, IRF1 &amp; IRF9. Damaging mutations (including missense mutations) in any of these genes was considered as loss of pathway activity. We used the best observed response (measured by RECIST v1.1 criteria across time points) to define outcome. Complete and partial response were categorized as responses (R), while stable and progressive disease were categorized as non-responses (NR). The percentage change in target lesions (CTL) was also assessed. Response data was analyzed using additive logistic (LogR) and linear regression (LinR). Overall survival (OS) and progression-free survival (PFS) were analyzed using Cox proportional-hazards models. Three predictor variable groups were established; the final models comprised IFN-γ pathway loss, tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) tumor proportion score (TPS), prior platinum chemotherapy, age, sex, histological subtype, initial tumor stage, and smoking pack years. Results: 309 patients had evaluable whole-exome sequencing data, treatment with PD-(L)1 inhibitor regimens, and matched radiological responses (121 R [39%], 188 NR [61%]); 306 had matched OS data; 297 had matched PFS data; and 227 had matched CTL data. 27 (7%) patients had damaging mutations in the IFN-γ pathway [IFNGR1 (3); IFNGR2 (1); JAK1 (6); JAK2 (8); STAT1 (3); IRF1 (1); IRF9 (3); and JAK2 &amp; IRF9 (1)]. Significant relationships were observed in univariable models between IFN-γ pathway loss and both response (LogR: OR 0.17, p=2 × 10−4*; LinR: OR 0.70, p=4.8 × 10−4*) and survival (PFS: HR 0.45, p=0.0067*; OS: HR 0.51, p=0.037*). IFN-γ pathway loss is significantly associated with TMB (p=9.7 × 10−8*, Wilcoxon signed rank test). Multivariable models incorporating TMB and PD-L1 TPS confirmed the significance of IFN-γ pathway loss in both response (LogR: OR 0.29, p=0.050*; LinR: OR 0.71, p=0.0052*) and survival (PFS: HR 0.37, p=0.036*; OS: HR 0.27, p=0.030*). These relationships persisted in the final multivariable models (LogR: OR 0.32, p=0.11; LinR: OR 0.69, p=0.012*; PFS: HR 0.39, p=0.068; OS: HR 0.15, p=0.0038*). Random gene sets, of similar total protein coding sequence length, significantly associated with TMB but not with multivariable models of response or survival. Conclusion: In this NSCLC cohort, damaging IFN-γ pathway mutations associate with increased immunotherapy sensitivity, independently of TMB and other co-variates, as evidenced by multiple clinical outcomes. Citation Format: Alexander Azizi, Arvind Ravi, Natalie Vokes, Stephen-John Sammut, Justin Gainor, Gad Getz. Increased immunotherapy sensitivity associated with damaging IFN-gamma pathway mutations in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1169.

Abstract 3559: A validated bioinformatics tool-set for predicting TCR specificity

Abstract Background: The T-cell receptor (TCR) is a transmembrane receptor on T cells, which is responsible for recognizing any foreign antigen derived peptide, presented on infected or abnormal cells by the MHC-I complex. Predicting TCR specificity computationally is a long-standing problem. Many methods leverage large libraries of TCR sequences and cognate epitopes as input for machine learning and predict specificity from sequence alone. Most methods generally do not perform very well, especially in the case of unseen peptides, i.e. for cases where peptides were not part of the input library. Methods: Here we describe three independent but interacting in-silico modules for TCR discovery. 1) The first module predicts specificity from deep phenotypic T cell profiles. We produce these profiles using a high-throughput pipeline, which combines multiplexed CyTOF with subsequent VDJ CITE-Seq. The generated multiomics data is aggregated into a database called TCAPS, which is the basis for machine learning models. These models can predict T cell specificity from the deep phenotype alone, i.e. without TCR sequence information. The resolution however is limited to the target class and hence the actual epitope has to be determined through subsequent deorphanization. This deorphanization step is augmented by the other two modules also described here. 2) The second module enables ultrafast sequence similarity search, which is used to find similar TCRs with known epitope specificity 3) The third module ranks TCR candidates for prioritisation for in-vitro validation. This is achieved by running the Alphafold-based structure prediction program TCRDock. Results: 1) We demonstrate how TCR specificity can be correctly predicted from multiomics deep T-cell profiles and specifically highlight two in-vitro validated predictions, for which the corresponding epitope/antigen were not part of the input panel. 2) Using a recently published TCR clustering benchmark TCRScapes, we demonstrate the superior performance of our ultrafast sequence similarity search. 3) We furthermore showcase TCRdock-directed experiments on TCRs specific for SF3B1mut-induced splice variants. TCRdock correctly ranked TCR candidates, including artificial combinations, whose functionality we prove through in-vitro validation. Conclusions: We describe three modules for TCR discovery and demonstrate their utility by benchmarking and in-vitro validation of multiple predictions, including two examples against unseen epitopes/antigens. Citation Format: Andreas Wilm, Florian Schmidt, Melissa Wirawan, Manisha Cooray, Kan Xing Wu, Katja Fink, Dan MacLeod. A validated bioinformatics tool-set for predicting TCR specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3559.

Abstract 2295: Circulating T cell receptor repertoire analysis improves cancer early detection

Abstract Background: Cancer screening by liquid biopsy promises to detect cancer early when it can be cured. However, current liquid biopsy approaches based on circulating tumor DNA (ctDNA) detect only a minority of early-stage cancers, because these tumors shed little ctDNA and plasma sample volume is limited. For liquid biopsy to realize its potential for the early detection of cancer, sensitivity for the earliest stage disease must be improved. Methods: We set out to improve sensitivity of liquid biopsy for cancer early detection by exploiting tumor recognition by T cells through sequencing of the circulating T cell receptor repertoire. Using gDNA extracted from blood buffy coats, we studied a cohort of 451 lung cancer patients (86% with stage I disease) and 525 subjects without cancer, enriched for older individuals with a history of smoking. We performed genomic DNA TCR beta chain sequencing to yield a median of 81,088 TCR clonotypes in cancer patients and 112,588 clonotypes in non-cancer controls. We built a TCR sequence similarity nearest neighbor graph to cluster &amp;gt;68M distinct TCR clonotypes into TCR repertoire functional units (RFUs). The TCR frequencies of RFUs were tested for association with cancer status and significantly associated RFUs were combined using a support vector machine model into a TCR RFU cancer score. This model was evaluated by 10-fold cross-validation (CV) and compared to a ctDNA panel of 237 mutation hotspots in 154 lung cancer driver genes (IDT™) and to a panel of 17 lung cancer related protein biomarkers (Olink®) in 87 subjects. Results: We identified 177 cancer-associated TCR RFUs at FDR≤0.10, including 83 RFUs that were enriched in cancer samples with a TCR count log2-fold change between 0.05 and 1.31, and 94 that were enriched in controls with a log2-fold change between 0.07 and 0.40. We observed 56 of the 83 cancer-enriched RFUs had decreasing TCR counts with increasing age across the cohort, coupled with higher counts in cancer patients relative to age-matched controls. The TCR RFU cancer score achieved a cross-validated ROC AUC of 0.69 for cancer status prediction, with the AUC for stage I cancers similar to that of stage II-IV (0.69 vs. 0.70). 44% of stage I cancers (test samples of each CV fold) were detected by the TCR model at a specificity of 80%. We saw a substantial gain in sensitivity for stage I cancer when TCR RFUs were added to ctDNA and proteins, with a ~15-20%-point increase seen at the 90% target specificity level typical for cancer screening tests. Likewise, sensitivity increased at the 99% specificity level used in multi-cancer early detection, with 35% of stage I lung cancers detected, which could not be achieved by any analyte alone. Conclusion: We demonstrate detection of a significant fraction of early lung cancer cases from blood by analyzing the circulating TCR repertoire and show that this signal is complementary to established analytes such as proteins and ctDNA. Citation Format: Yilong Li, Michelle Nahas, Dennis Stephens, Kate Froburg, Emma Hintz, Devin Champagne, Amaneet Lochab, Markus Brown, Jesse Abhyankar, Jasper Braun, Michelle Dinh, María Antonia Fortuño, María-del-Mar Ocon, Andrea Pasquier, Inés María Luque, Christopher W. Seder, Jeffrey A. Borgia, Luis Miguel Seijo, Luis M. Montuenga, Roman Yelensky. Circulating T cell receptor repertoire analysis improves cancer early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2295.

Abstract 2837: Conservation of JAK-STAT signaling in canine lymphocytes responding to human IL-6, IL-7, and IL-1 alpha

Abstract Dogs share physiological similarities with humans, positioning them as valuable models for studying the efficacy and safety of drugs and their underlying mechanisms of action. The JAK-STAT pathway, a highly conserved signaling pathway across species, including humans and dogs, presents an opportunity to investigate the therapeutic potential of cytokines that utilize this pathway. In this study, we aimed to evaluate the functional activity of three canine cytokines on canine cells. To assess stimulation of the JAK/STAT pathway, we used a flow cytometry assay that measures phospho-STAT in activated canine or human lymphocytes in whole blood specimens. Testing of commercially available human and canine IL-6 proteins revealed that canine lymphocytes respond poorly to most canine IL-6 products, whereas all recombinant human IL-6 products stimulated STAT phosphorylation in canine lymphocytes, despite sharing low sequence similarity with canine IL-6. In human T cells, both canine IL-6 and human IL-6 stimulated STAT phosphorylation, demonstrating biological activity from the canine IL-6 products. Several JAK inhibitors inhibited cytokine-induced STAT phosphorylation in both canine and human CD4 T-cells, consistent with JAK inhibition, but targeting the IL-6 receptor with tocilizumab only blocked IL-6-induced STAT phosphorylation in human but not canine CD4 T-cells in whole blood. These findings demonstrate the potential of our assay system to assess cross-species cytokine reactivity, determine if JAK/STAT inhibitors used clinically might be useful in treating inflammatory conditions in dogs, and identify novel canine-specific inhibitors. Additionally, we explored the induction of IL-8 by human and canine IL-1 alpha in IL-1 alpha sensitive human cells. Human IL-1 alpha failed to stimulate a canine IL-1 alpha sensitive cell line, suggesting species-specific differences in IL-1 alpha activity. Furthermore, we discovered that canine IL-7 and human IL-7 are species-specific. In summary, this proof-of-concept study highlights the capability to assess the activation of STATs and pathway inhibition by JAK inhibitors in canine whole blood using flow cytometry. We confirmed that human IL-6 stimulates JAK/STAT signaling in canine CD4 T-cells. Our results also revealed significant differences in species cross-reactivity of human IL-6, IL-7, and IL-1 alpha, underscoring the importance of species-specific investigations. Leveraging the similarities and differences between canine and human cytokines targeting the JAK-STAT pathway can enhance drug development efforts and contribute to the advancement of safer and more effective therapies for both humans and pet dogs with similar diseases. This study was funded by NCI Contract No. 75N91019D00024. Citation Format: King Leung Fung, Melissa L. Breiner, James B. Mitchell, Ralph E. Parchment. Conservation of JAK-STAT signaling in canine lymphocytes responding to human IL-6, IL-7, and IL-1 alpha [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2837.

Abstract 6282: Novel androgen related gene network in prostate cancer cell model

Abstract Prostate cancer stands as the foremost cause of cancer-related deaths among men in the United States, with the primary treatment being androgen receptor (AR) antagonists relying on current androgen signaling knowledge. Most of this knowledge is derived from next-generation sequencing experiments, but the oversight of splice variants and highly similar sequences in multiple loci can hinder a comprehensive understanding. In an effort to unveil the complete androgen receptor transcriptional program, we employed a novel sequencing analysis protocol. Using a R1881-LNCaP model, we identified 212 upregulated and 92 downregulated genes, of which a notable one-third (68 upregulated and 39 downregulated) had not been previously associated with androgen. Among these novel transcripts, 5.6% lacked splice variants, while 16.3% exhibited one or more splice variants, including 3.8% from seven unique gene families. Recognized androgen-regulated genes, such as ORM1, STEAP4, NPPC, ORM2, and KLK3, were validated as upregulated, while GRB10, FN1, PPFIA2, and PLA2G2A were confirmed as downregulated in this in-vitro model. Utilizing chromatin immunoprecipitation-sequencing (ChIP-seq) data, we found that 13% of the genes contained at least one androgen response element within 5 kb upstream or downstream. Further validation through real-time PCR confirmed the expression levels of 90% of the selected genes (18). Ingenuity Pathway Analysis revealed two major regulators (ESRRA and ARFGEF2) in causal networks likely interacting with the novel genes. Intriguingly, 57.1% of ESRRA-involved genes contained at least one ESRRA binding site in a ChIP-seq dataset. Although ESRRA is known to activate androgen biosynthesis in castration-resistant prostate cancer, its interaction with AR and co-regulation of androgen signaling remain unknown. Ongoing studies involve the characterization of these networks in prostate cancer cell lines, examining anchorage-independent capacity, invasion, migration, proliferation, and progression. Data collection and comparison will ascertain whether the signaling is driven by mutated AR, and the role of ARv7 will be elucidated through gene lockdown experiments. Citation Format: Alex L. McDonald, Spencer Snow, Shuk-mei Ho, Yuet-kin Leung. Novel androgen related gene network in prostate cancer cell model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6282.

Childhood cancer mutagenesis caused by transposase-derived PGBD5.

Genomic rearrangements are a hallmark of most childhood tumors, including medulloblastoma, one of the most common brain tumors in children, but their causes remain largely unknown. Here, we show that PiggyBac transposable element derived 5 (Pgbd5) promotes tumor development in multiple developmentally accurate mouse models of Sonic Hedgehog (SHH) medulloblastoma. Most Pgbd5-deficient mice do not develop tumors, while maintaining normal cerebellar development. Ectopic activation of SHH signaling is sufficient to enforce cerebellar granule cell progenitor-like cell states, which exhibit Pgbd5-dependent expression of distinct DNA repair and neurodevelopmental factors. Mouse medulloblastomas expressing Pgbd5 have increased numbers of somatic structural DNA rearrangements, some of which carry PGBD5-specific sequences at their breakpoints. Similar sequence breakpoints recurrently affect somatic DNA rearrangements of known tumor suppressors and oncogenes in medulloblastomas in 329 children. This identifies PGBD5 as a medulloblastoma mutator and provides a genetic mechanism for the generation of oncogenic DNA rearrangements in childhood cancer.

Cardiovascular disease healthcare trajectories: descriptions, similarities, mortality rates of heart failure in France.

The primary objectives of this study were to analyse the nationwide healthcare trajectories of heart failure (HF) patients in France, 2years after their first hospitalization, and to measure sequence similarities. Secondary objectives were to identify the association between trajectories and the risk of mortality. A retrospective, observational study was conducted using data extracted from the Echantillon Généraliste des Bénéficiaires database, covering the period from 1 January 2008 to 31 December 2018. Follow-up concluded upon death or at the end of the study. We developed a methodology specific to healthcare data by extracting frequent healthcare trajectories and measuring their similarity for use in a survival machine learning analysis. In total, 11488 HF patients were included and followed up for an average of 2.9±1.3years. The mean age of the patients was 78.0±13.2years. The first-year mortality rate was 31.7% and increased to 78.8% at 5years. Fifty per cent of patients experienced re-hospitalization for reasons related to cardiovascular diseases. We identified 1707 hospitalization sequences, and 21 sequences were associated with survival, while 15 sequences were linked to mortality. In all our models, age and gender emerged as the most significant predictors of mortality (permutation feature importance: 0.099±0.00078 and 0.0087±0.00018, respectively; weights could be interpreted in relative terms). Specifically, the age at initial hospitalization for HF was positively associated with mortality. Gender (male: 49.5%) was associated with poorer prognoses. Healthcare trajectories, including non-surgical device treatments, valve replacements, and atrial fibrillation ablation, were associated with a better prognosis (permutation feature importance: 0.0047±0.00011, 0.0014±0.000073, and 0.00095±0.000097, respectively), except in cases where these invasive treatments preceded or followed hospitalization for cardiac decompensation. The predominant negative prognosis sequences were mostly those that included HF-related hospitalizations before or after other-related hospitalizations (permutation feature importance: 0.0007±0.000091 and 0.00011±0.000045, respectively). We highlight the value of healthcare trajectories on frequent hospitalization sequences, mortality, and prognosis and indicate the necessary prognostic value of HF re-hospitalization. Our work may be an essential tool for better identification of at-risk patients in order to increase and improve personalized care in the future.

Oleispirillum naphthae gen. nov., sp. nov., a bacterium isolated from oil sludge, and proposal of Oleispirillaceae fam. nov.

A microaerophilic, Gram-negative, motile, and spiral-shaped bacterium, designated Y-M2T, was isolated from oil sludge of Shengli oil field. The optimal growth condition of strain Y-M2T was at 25 °C, pH 7.0, and in the absence of NaCl. The major polar lipid was phosphatidylethanolamine. The main cellular fatty acid was iso-C17 : 0 3-OH. It contained Q-9 and Q-10 as the predominant quinones. The DNA G+C content was 68.1 mol%. Strain Y-M2T showed the highest 16S rRNA gene sequence similarity to Telmatospirillum siberiense 26-4bT (91.1 %). Phylogenetic analyses based on 16S rRNA gene and genomes showed that strain Y-M2T formed a distinct cluster in the order Rhodospirillales. Genomic analysis showed that Y-M2T possesses a complete nitrogen-fixation cluster which is phylogenetically close to that of methanogene. The nif cluster, encompassing the nitrogenase genes, was found in every N2-fixing strain within the order Rhodospirillales. Phylogeny, phenotype, chemotaxonomy, and genomic results demonstrated that strain Y-M2T represents a novel species of a novel genus in a novel family Oleispirillaceae fam. nov. in the order Rhodospirillales, for which the name Oleispirillum naphthae gen. nov., sp. nov. was proposed. The type strain is Y-M2T (=CCAM 827T=JCM 34765T).

Functional analysis of CYP71AV1 reveals the evolutionary landscape of artemisinin biosynthesis.

Artemisinin biosynthesis, unique to Artemisia annua, is suggested to have evolved from the ancestral costunolide biosynthetic pathway commonly found in the Asteraceae family. However, the evolutionary landscape of this process is not fully understood. The first oxidase in artemisinin biosynthesis, CYP71AV1, also known as amorpha-4,11-diene oxidase (AMO), has specialized from ancestral germacrene A oxidases (GAOs). Unlike GAO, which exhibits catalytic promiscuity toward amorpha-4,11-diene, the natural substrate of AMO, AMO has lost its ancestral activity on germacrene A. Previous studies have suggested that the loss of the GAO copy in A. annua is responsible for the abolishment of the costunolide pathway. In the genome of A. annua, there are two copies of AMO, each of which has been reported to be responsible for the different product profiles of high- and low-artemisinin production chemotypes. Through analysis of their tissue-specific expression and comparison of their sequences with those of other GAOs, it was discovered that one copy of AMO (AMOHAP) exhibits a different transcript compared to the reported artemisinin biosynthetic genes and shows more sequence similarity to other GAOs in the catalytic regions. Furthermore, in a subsequent in vitro enzymatic assay, the recombinant protein of AMOHAP unequivocally demonstrated GAO activity. This result clearly indicates that AMOHAP is a GAO rather than an AMO and that its promiscuous activity on amorpha-4,11-diene has led to its misidentification as an AMO in previous studies. In addition, the divergent expression pattern of AMOHAP compared to that of the upstream germacrene A synthase may have contributed to the abolishment of costunolide biosynthesis in A. annua. Our findings reveal a complex evolutionary landscape in which the emergence of a new metabolic pathway replaces an ancestral one.