Biology subjects

Weber, C. R.

Publications and source records attributed to Weber, C. R..

At least 19 recordsLinked to original sources

bioRxiv2026

An engineered Clostridial mini consortium modulates intestinal inflammation

Modern lifestyle factors have altered gut microbiota composition and function. Bacteria in the Clostridia class modulate mucosal immune responses through various mechanisms including production of secondary bile acids (SBA). Here, we present a novel system to study how the SBA isodeoxycholic acid (isoDCA) regulates host immunity. Through targeted mutagenesis of bile acid epimerization genes, we engineered Ruminococcus gnavus to ablate isoDCA production. Combining R. gnavus (WT or KO) with Peptacetobacter hiranonis created a two-member consortium that toggles isoDCA production on or off while keeping all other variables constant. Using this system, we demonstrate that isoDCA induces colonic lamina propria ROR{gamma}t{square} Foxp3{square} regulatory T cells (pTregs) through a mechanism requiring both the Takeda G protein-coupled receptor 5 (TGR5) and the Farnesoid X receptor (FXR). Engraftment of this isoDCA+ consortium protected against colitis in an adoptive T cell transfer model by reshaping the microbiota and suppressing host inflammation.

immunology↗

bioRxiv2026

Cryo-electron tomography reveals paracellular claudin-15 pores at the tight junction

Tight junctions (TJs) are composed of anastomosing strands between epithelial cells. Members of the claudin family of proteins reside within TJ strands and either seal the paracellular space or assemble into charge and size-selective pathways. Functional studies suggest that claudin-mediated conductance pathways resemble traditional ion channels. However, such postulated pores have not been directly visualized. Using a model claudin deficient epithelium where exogenously introduced EGFP-CLDN15 is the only claudin family member expressed, our investigation sheds light on the arrangement and structure of the postulated claudin pores. Following correlative light and electron microscopical identification of TJs and cryo-electron tomography, we identified series of linearly distributed electron lucent features that locate between two closely apposed plasma membranes of adjacent cells. At these sites, the median spacing between adjacent features is 2.25 nm (IQR = 1.83), with a median 1.66 nm (IQR = 0.92) diameter. In contrast, such features were not observed in claudin deficient model epithelium with exogenous mCherry-ZO-1 expression. These findings agree with the postulated and extensively modeled claudin pores formed within the simple columnar epithelium. This provides the first direct evidence of paracellular pore organization and paves way for future biophysical investigation. SIGNIFICANCEBy combining correlative fluorescence imaging, FIB milling, and cryo-ET within an epithelial system restricted to a single claudin isoform, we were able to visualize repetitive, low-density pore features within CLDN15-containing tight junctions (TJs), structures not previously resolved in intact epithelia. These features were absent in claudin-negative controls and displayed placement and geometry consistent with CLDN15 X-ray crystallography and molecular dynamics models. Quantitative measurements of pore diameter, paracellular gap width, and pore spacing further support their assignment as CLDN15 pores. These findings establish a structurally validated platform for defining claudin pore ultrastructure and provide a foundation for future efforts to compare pore-forming and barrier-forming claudins, understand disease-associated junction remodeling, and guide therapeutic modulation of epithelial barrier function.

physiology↗

bioRxiv2025

Tumor nutrient stress gives rise to a drug tolerant cell state in pancreatic cancer

Systemic therapies are the standard of care for most pancreatic ductal adenocarcinoma (PDAC) patients but provide limited benefit due to pervasive resistance. The fibrotic tumor microenvironment (TME) is thought to drive resistance by restricting perfusion and drug delivery. Here, we show that therapeutically relevant drug concentrations are achieved even in poorly perfused, therapy-resistant murine PDAC tumors, indicating that impaired delivery alone does not explain drug resistance. Instead, we find TME exposure imprints a therapy-resistant state upon PDAC cells. These observations raised the question of how the TME imposes this state. Poor perfusion alters nutrient availability in the TME. To model this, we developed Tumor Interstitial Fluid Medium (TIFM), which recapitulates TME nutrient conditions. TIFM cultured PDAC cells acquire a therapy-resistant phenotype that mirrors resistance observed in the TME. In this state, cytotoxic and targeted therapies retain on-target activity but fail to trigger cell death, resulting in therapeutic tolerance. Mechanistically, drug tolerance is driven by suppression of apoptotic priming and can be reversed by inhibition of the anti-apoptotic regulator BCL-XL. These results identify TME-driven reprogramming of cell death as a key mechanism of therapy resistance in PDAC and establish TIFM as a physiologically relevant model for studying microenvironment-induced drug resistance.

cancer biology↗

bioRxiv2025

Histopathologic Evaluation and Single-Cell Spatial Transcriptomics of the Colon Reveal Cellular and Molecular Abnormalities Linked to J-Pouch Failure in Patients with Inflammatory Bowel Disease.

Background and AimsTotal abdominal colectomy (TAC) with a staged ileal pouch-anal anastomosis (IPAA) is a common surgical treatment for ulcerative colitis (UC). However, a significant percentage of patients experience pouch failure, leading to morbidity. This retrospective case-control study identified histopathological features of the TAC specimen associated with pouch failure and investigated the molecular mechanisms of this susceptibility using single-cell spatial transcriptomics. MethodsWe analyzed a cohort of 417 patients who underwent IPAA between 2000-2010 at the University of Chicago Medical Center for up to 18 years. Histological examination of TAC specimens focused on disease activity, depth of inflammation, and specific features, including granulomas and deep ulcers. A subset of patients was profiled using single-cell spatial transcriptomics to map gene expression and immune cell interactions in relation to the risk of pouch failure. ResultsThe 18-year pouch failure risk was 23%, with post-procedure diagnosis of CD as a major risk factor (HR = 4.3, 95% CI: 2.3-8.1) as well as high-risk histologic features, including deep chronic inflammation (HR = 21, 95% CI: 11-41) and severe disease activity (HR = 14, 95% CI: 5.7-32) in TAC specimens. Spatial transcriptomics showed immune infiltration of T and myeloid cells, reduced myocyte-glial interactions, and cytokine signaling pathways such as IL-10, IL-1{beta}, and type I/II interferons, associated with an increased risk of pouch failure. ConclusionHistological features and spatial molecular profiling are predictive of IPAA failure. These findings support the use of histologic evaluation and targeted molecular analysis of the TAC specimen to identify high-risk patients and improve IPAA outcomes.

pathology↗

bioRxiv2024

Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2

The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike proteins receptor binding domain (RBD), crucial for host cell invasion, while the virus evolves to evade antibody recognition. Here, we establish a synthetic coevolution system combining high-throughput screening of antibody and RBD variant libraries with protein mutagenesis, surface display, and deep sequencing. Additionally, we train a protein language machine learning model that predicts antibody escape to RBD variants. Synthetic coevolution reveals antagonistic and compensatory mutational trajectories of neutralizing antibodies and SARS-CoV-2 variants, enhancing the understanding of this evolutionary conflict.

synthetic biology↗

bioRxiv2023

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies. AvailabilityATAT is available at: https://github.com/StevenSong/tissue-alignment

bioinformatics↗

bioRxiv2023

Untangling the Role of Pathobionts from Bacteroides Species in Inflammatory Bowel Diseases

Inflammatory bowel diseases (IBD) arise from a convergence of underlying genetic susceptibility, environmental factors, and shifts in gut microbiota function and membership. Although the latter may trigger and contribute to IBD, there is little consensus on a specific causative pathogen. In this study, we demonstrate that commensal Bacteroides fragilis strains from ulcerative colitis (UC) patients before and during the development of ileal pouchitis engraft and promote colitis in specific pathogen free (SPF) IL-10 deficient (IL-10-/-) mice, but not in wild type SPF mice or when mono-associated in germ free mice. The colitis in IL-10-/- mice was also associated with significant alterations in commensal microbiota potentially important for maintaining intestinal and immune homeostasis. UC pouchitis B. fragilis also engrafts in DSS-induced colitis in WT SPF mice, indicating a fitness advantage under conditions of mucosal inflammation over other commensals in the gut microbiota. These findings show that gut inflammation promotes the expansion and fitness of UC-derived Bacteroides species that is associated with changes in the SPF gut microbiota and may be promote colitis in genetically susceptible hosts. ImportanceThis study supports the notion that human inflammatory bowel diseases arise from the emergence of indigenous pathobionts in genetically-prone subjects. Colitis-promoting pathobionts are well-suited to establish themselves in the host inflammatory environment and outcompete endogenous microbiota. Once engrafted, the pathobiont can further aggravate inflammation in a genetically-susceptible host. Such complex interplay among several factors creates a vicious pro-inflammatory cycle and promotes disease development. These findings are consistent with our previous clinical observation that B. fragilis, an otherwise low-abundance commensal species, expands prior to the development of UC pouchitis. We believe these findings are relevant to the pathogenesis of UC pouchitis and possibly human inflammatory bowel diseases in general, underscoring the role of commensal to pathobiont transitions, rather than classical pathogens, in promoting and exacerbating the onset of human IBD.

microbiology↗

bioRxiv2023

Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants

Most COVID-19 antibody therapies rely on binding the SARS-CoV-2 receptor binding domain (RBD). However, heavily mutated variants such as Omicron and its sublineages, which are characterized by an ever increasing number of mutations in the RBD, have rendered prior antibody therapies ineffective, leaving no clinically approved antibody treatments for SARS-CoV-2. Therefore, the capacity of therapeutic antibody candidates to bind and neutralize current and prospective SARS-CoV-2 variants is a critical factor for drug development. Here, we present a deep learning-guided approach to identify antibodies with enhanced resistance to SARS-CoV-2 evolution. We apply deep mutational learning (DML), a machine learning-guided protein engineering method to interrogate a massive sequence space of combinatorial RBD mutations and predict their impact on angiotensin-converting enzyme 2 (ACE2) binding and antibody escape. A high mutational distance library was constructed based on the full-length RBD of Omicron BA.1, which was experimentally screened for binding to the ACE2 receptor or neutralizing antibodies, followed by deep sequencing. The resulting data was used to train ensemble deep learning models that could accurately predict binding or escape for a panel of therapeutic antibody candidates targeting diverse RBD epitopes. Furthermore, antibody breadth was assessed by predicting binding or escape to synthetic lineages that represent millions of sequences generated using in silico evolution, revealing combinations with complementary and enhanced resistance to viral evolution. This deep learning approach may enable the design of next-generation antibody therapies that remain effective against future SARS-CoV-2 variants.

immunology↗

bioRxiv2023

Molecular dynamics analyses of CLDN15 pore size and charge selectivity

Claudin-15 (CLDN15) molecules form channels that directly regulate cation and water transport. In the gastrointestinal tract, this transport indirectly impacts nutrient absorption. However, the mechanisms governing ion transport through these channels remain poorly understood. We addressed this question by building on our previous cell culture studies and all atom molecular dynamic simulation model of CLDN15. By mutating D55 to a bulkier glutamic acid (E) or neutral amino acid asparagine (N), our in vitro measurements showed that the D55E mutation decreased charge selectivity and favored small ion permeability, while the D55N mutation led to reduced charge selectivity without markedly altering size selectivity. By establishing a simplified (reduced) CLDN15 molecular dynamics model that excludes non-essential transmembrane regions, we were able to probe how D55 modified cation dehydration, charge interaction, and permeability. These results provide novel insight into organization of the CLDN15 selectivity filter and suggests that D55 plays a dual role in shaping both electrostatic and steric properties of the pore, but its electrostatic role is more prominent in determining CLDN15 cation permeability. This knowledge can be used toward the development of effective strategies to modulate CLDN15 function. The experimental approach established can be further extended to study the function of other claudin channels. Together, these advancements will help us to modulate tight junctions to promote human health. SUMMARYCell culture and molecular dynamics simulations reveal the role of the CLDN15-D55 residue in ion size and charge selectivity. A reduced CLDN15 model offers novel insights into ion conductance, providing a valuable tool for therapeutic modulation of tight junctions.

physiology↗

bioRxiv2023

A novel triptolide analog downregulates NF-κB and induces mitochondrial apoptosis pathways in human pancreatic cancer

BackgroundPancreatic cancer is the seventh leading cause of cancer-related death worldwide, and despite advancements in disease management, the 5-year survival rates stands at only 9%. Triptolides have potent anti-tumor activity against different types of cancers, including pancreatic cancer, however poor solubility and toxicity limit their translation into clinical use. MethodsWe synthesized a novel pro-drug of triptolide, (E)-19-[(1-benzoyloxy-1-phenyl)-methylidene]-Triptolide (CK21), and formulated into an emulsion for in vitro and in vivo testing in rats and mice, and using human pancreatic cancer cell lines and patient-derived pancreatic tumor organoids. A time-course transcriptomic profiling of tumor organoids treated with CK21 in vitro was conducted to define its mechanism of action, as well as transcriptomic profiling at a single time point post-CK21 administration in vivo. FindingsIntravenous administration of emulsified CK21 resulted in the stable release of triptolide, and potent anti-proliferative effects on human pancreatic cancer cell lines and patient-derived pancreatic tumor organoids in vitro, and with minimal toxicity in vivo. Time course transcriptomic profiling of tumor organoids treated with CK21 in vitro revealed <10 differentially expressed genes (DEGs) at 3 h and [~]8,000 DEGs at 12 h. Overall inhibition of general RNA transcription was observed, and Ingenuity pathway analysis together with functional cellular assays confirmed inhibition of the NF-{kappa}B pathway, increased oxidative phosphorylation and mitochondrial dysfunction, leading ultimately to increased reactive oxygen species (ROS) production, reduced B-cell-lymphoma protein 2 (BCL2) expression, and mitochondrial-mediated tumor cell apoptosis. InterpretationCK21 is a novel pro-drug of triptolide that exerts potent anti-proliferative effects on human pancreatic tumors by inhibiting the NF-{kappa}B pathway, leading ultimately to mitochondrial-mediated tumor cell apoptosis. FundingThe study of the anti-tumor efficacy of CK21 supported in part by a research grant from Cinkate Pharmaceutical Corp; the funders had no role in the study design, interpretation or decision to publish. Patient-derived pancreatic tumor organoids were a generous gift from the Organoid and Primary Culture Research Core at University of Chicago.

pharmacology and toxicology↗

bioRxiv2022

Intestinal Epithelial Heat Shock Protein 25/27 integrates host and microbial drivers of mucosal restitution following inflammatory injury

Mucosal healing following inflammatory injury is poorly understood and often neglected, despite being the best indicator of long-term outcomes in inflammatory bowel diseases. We report here that the enigmatic small molecular weight heat shock protein, Hsp25 (the human form is Hsp27), plays a vital role in converging microbial and host factors to promote pSTAT3-mediated mucosal healing. In wild type mice, the proximal-to-distal gradient of intestinal epithelial cell (IEC) Hsp25 expression is dependent on microbial cues. Patients with left-sided ulcerative colitis, however, show reduced levels of Hsp27 expression in both uninvolved and involved areas compared to normal colons of non-IBD patients. In mice with global or IEC-specific Hsp25 gene-targeted deletion, impaired mucosal healing with development of hallmarks of chronic disease are observed following DSS-induced or TNBS-induced colitis, whereas mucosal restitution is accelerated in IEC-specific overexpressing Hsp25 transgenic mice. In colonic IECs derived from these murine lines, Hsp25 binds and stabilizes a phospho-STAT3/YAP nuclear complex stimulated by IL-22 to sustain its wound healing gene programming. Thus, our findings provide insight into the mechanism of action of IEC Hsp25/27 in integrating host and microbial drivers of mucosal restitution, which can be leveraged to develop novel approaches for achieving and maintaining remission in complex immune disorders like IBD.

pathology↗

bioRxiv2022

Pancreatic tumors activate arginine biosynthesis to adapt to myeloid-driven amino acid stress

Nutrient stress in the tumor microenvironment requires cancer cells to adopt adaptive metabolic programs to maintain survival and proliferation. Therefore, knowledge of microenvironmental nutrient levels and how cancer cells cope with such nutrition is critical to understand the metabolism underpinning cancer cell biology. Previously, we performed quantitative metabolomics of the interstitial fluid (the local perfusate) of murine pancreatic ductal adenocarcinoma (PDAC) tumors to comprehensively characterize nutrient availability in the microenvironment of these tumors (Sullivan et al., 2019a). Here, we develop Tumor Interstitial Fluid Medium (TIFM), a cell culture medium that contains nutrient levels representative of the PDAC microenvironment, enabling study of PDAC metabolism under physiological nutrition. We show that PDAC cells cultured in TIFM, compared to standard laboratory models, adopt a cellular state more similar to PDAC cells in tumors. Further, using the TIFM model we identified arginine biosynthesis as a metabolic adaptation PDAC cells engage to cope with microenvironmental arginine starvation driven by myeloid cells in PDAC tumors. Altogether, these data show that nutrient availability in tumors is an important determinant of cancer cell metabolism and behavior, and cell culture models that incorporate physiological nutrient availability have improved fidelity and enable the discovery of novel cancer metabolic phenotypes.

cancer biology↗

bioRxiv2022

Reference-based comparison of adaptive immune receptor repertoires

B- and T-cell receptor (immune) repertoires can represent an individuals immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters (e.g., clonal diversity, germline usage). Here, we introduce immuneREF: a quantitative multi-dimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. immuneREF is implemented in an R package and was validated based on detection sensitivity of immune repertoires with known similarities and dissimilarities. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2400 datasets from individuals with varying immune states (healthy, [autoimmune] disease and infection [Covid-19], immune cell population). Importantly we discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF implements population-wide analysis of immune repertoire similarity and thus enables the study of the adaptive immune response across health and disease states.

systems biology↗

bioRxiv2021

Predictive profiling of SARS-CoV-2 variants by deep mutational learning

The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1-4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

synthetic biology↗

bioRxiv2021

Molecular mechanism of claudin-15 strand flexibility

Claudins are one of the major components of tight junctions that play a key role in formation and maintaining epithelial barrier function. Tight junction strands are dynamic and capable of adapting their structure in response to large-scale tissue rearrangement and cellular movement. Here, we present molecular dynamics simulations of claudin-15 strands of up to 225 nm in length in two parallel lipid membranes and characterize their mechanical properties. The persistence length of claudin-15 strands is comparable with experiments leading to a curvature of 0.12 nm-1 at room temperature. Our results indicate that lateral flexibility of claudin strands is due to an interplay of three sets of interfacial interaction networks between four linear claudin strands in the membranes. In this model, claudins are assembled into interlocking tetrameric ion channels along the strand that slide with respect to each other as the strands curve over sub-micrometer length scales. These results suggest a novel molecular mechanism underlying claudin-15 strand flexibility. It also sheds light on the inter-molecular interactions and their role in maintaining epithelial barrier function.

biophysics↗

bioRxiv2021

The Physiological Landscape and Specificity of Antibody Repertoires

Diverse antibody repertoires spanning multiple lymphoid organs (e.g., bone marrow, spleen, lymph nodes) form the foundation of protective humoral immunity. Changes in their composition across lymphoid organs are a consequence of B-cell selection and migration events leading to a highly dynamic and unique physiological landscape of antibody repertoires upon antigenic challenge (e.g., vaccination). However, to what extent B cells encoding identical or similar antibody sequences (clones) are distributed across multiple lymphoid organs and how this is shaped by the strength of a humoral response, remains largely unexplored. Here, we performed an in-depth systems analysis of antibody repertoires across multiple distinct lymphoid organs of immunized mice, and discovered that organ-specific antibody repertoire features (e.g., germline V-gene usage and clonal expansion profiles) equilibrated upon a strong humoral response (multiple immunizations and high serum titers). This resulted in a surprisingly high degree of repertoire consolidation, characterized by highly connected and overlapping B-cell clones across multiple lymphoid organs. Finally, we revealed distinct physiological axes indicating clonal migrations and showed that antibody repertoire consolidation directly correlated with antigen-specificity. Our study uncovered how a strong humoral response resulted in a more uniform but redundant physiological landscape of antibody repertoires, indicating that increases in antibody serum titers were a result of synergistic contributions from antigen-specific B-cell clones distributed across multiple lymphoid organs. Our findings provide valuable insights for the assessment and design of vaccine strategies.

immunology↗

bioRxiv2021

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody binding parameters. The simulation framework enables both the computation of antibody-antigen 3D-structures as well as functions as an oracle for unrestricted prospective evaluation of the antigen specificity of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (1D) data can be used to design native-like conformational (3D) epitope-specific antibodies, matching or exceeding the training dataset in affinity and developability variety. Furthermore, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Finally, we validated that the antibody design insight gained from simulated antibody-antigen binding data is applicable to experimental real-world data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design. HighlightsO_LIA large-scale dataset of 70M [3 orders of magnitude larger than the current state of the art] synthetic antibody-antigen complexes, that reflect biological complexity, allows the prospective evaluation of antibody generative deep learning C_LIO_LICombination of generative learning, synthetic antibody-antigen binding data, and prospective evaluation shows that deep learning driven antibody design and discovery at an unconstrained level is feasible C_LIO_LITransfer learning (low-N learning) coupled to generative learning shows that antibody-binding rules may be transferred across unrelated antibody-antigen complexes C_LIO_LIExperimental validation of antibody-design conclusions drawn from deep learning on synthetic antibody-antigen binding data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/451480v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1d947bcorg.highwire.dtl.DTLVardef@1f4c51corg.highwire.dtl.DTLVardef@b8df67org.highwire.dtl.DTLVardef@1530ea5_HPS_FORMAT_FIGEXP M_FIG We leverage large synthetic ground-truth data to demonstrate the (A,B) unconstrained deep generative learning-based generation of native-like antibody sequences, (C) the prospective evaluation of conformational (3D) affinity, paratope-epitope pairs, and developability. (D) Finally, we show increased generation quality of low-N-based machine learning models via transfer learning. C_FIG

immunology↗

bioRxiv2021

Individualized VDJ recombination predisposes the available Ig sequence space

The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individuals naive Ig repertoire, and consequently (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual-specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also non-genetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.

immunology↗