The dengue-specific immune response and antibody identification with machine learning.

Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.

Kidins220 regulates the development of B cells bearing the λ light chain.

The ratio between κ and λ light chain (LC)-expressing B cells varies considerably between species. We recently identified Kinase D-interacting substrate of 220 kDa (Kidins220) as an interaction partner of the BCR. In vivo ablation of Kidins220 in B cells resulted in a marked reduction of λLC-expressing B cells. Kidins220 knockout B cells fail to open and recombine the genes of the Igl locus, even in genetic scenarios where the Igk genes cannot be rearranged or where the κLC confers autoreactivity. Igk gene recombination and expression in Kidins220-deficient B cells is normal. Kidins220 regulates the development of λLC B cells by enhancing the survival of developing B cells and thereby extending the time-window in which the Igl locus opens and the genes are rearranged and transcribed. Further, our data suggest that Kidins220 guarantees optimal pre-BCR and BCR signaling to induce Igl locus opening and gene recombination during B cell development and receptor editing.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to TR and IG Gene Annotation.

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR) has revolutionized the ability to carry out large-scale experiments to study the adaptive immune response. Since the method was first introduced in 2009, AIRR sequencing (AIRR-Seq) has been applied to survey the immune state of individuals, identify antigen-specific or immune-state-associated signatures of immune responses, study the development of the antibody immune response, and guide the development of vaccines and antibody therapies. Recent advancements in the technology include sequencing at the single-cell level and in parallel with gene expression, which allows the introduction of multi-omics approaches to understand in detail the adaptive immune response. Analyzing AIRR-seq data can prove challenging even with high-quality sequencing, in part due to the many steps involved and the need to parameterize each step. In this chapter, we outline key factors to consider when preprocessing raw AIRR-Seq data and annotating the genetic origins of the rearranged receptors. We also highlight a number of common difficulties with common AIRR-seq data processing and provide strategies to address them.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis.

Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.

RWD-Cockpit: Application for Quality Assessment of Real-world Data.

BACKGROUND: Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. OBJECTIVE: To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. METHODS: The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. RESULTS: To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets-molecular, phenotypical, and social-and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies-de novo-generated sleep data and publicly available data sets-the RWD-Cockpit could identify and provide researchers with variables that might increase quality. CONCLUSIONS: The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores-quality identifiers-provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences.

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

The Real-World Data Challenges Radar: A Review on the Challenges and Risks regarding the Use of Real-World Data.

BACKGROUND: The life science industry has a strong interest in real-world data (RWD), a term that is currently being used in many ways and with varying definitions depending on the source. In this review article, we provide a summary overview of the challenges and risks regarding the use of RWD and its translation into real-world evidence and provide a classification and visualization of RWD challenges by means of the RWD Challenges Radar. SUMMARY: Based on a systematic literature search, we identified 3 types of challenges - organizational, technological, and people-based - that must be addressed when deriving evidence from RWD to be used in drug approval and other applications. It further demonstrates that numerous different aspects, for example, related to the application field and the associated industry, must be considered. A key finding in our review is that the regulatory landscape must be carefully assessed before utilizing RWD. KEY MESSAGES: Establishing awareness and insight into the challenges and risks regarding the use of RWD will be key to taking full advantage of the RWD potential. As a result of this review, an "RWD Challenges Radar" will support the establishment of awareness by providing a comprehensive overview of the relevant aspects to be considered when employing RWD.

Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics.

Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1-5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.

Single Cell Gene Expression Analysis in a 3D Microtissue Liver Model Reveals Cell Type-Specific Responses to Pro-Fibrotic TGF-ß1 Stimulation.

3D cell culture systems are widely used to study disease mechanisms and therapeutic interventions. Multicellular liver microtissues (MTs) comprising HepaRG, hTERT-HSC and THP-1 maintain multicellular interactions and physiological properties required to mimic liver fibrosis. However, the inherent complexity of multicellular 3D-systems often hinders the discrimination of cell type specific responses. Here, we aimed at applying single cell sequencing (scRNA-seq) to discern the molecular responses of cells involved in the development of fibrosis elicited by TGF-ß1. To obtain single cell suspensions from the MTs, an enzymatic dissociation method was optimized. Isolated cells showed good viability, could be re-plated and cultured in 2D, and expressed specific markers determined by scRNA-seq, qRT-PCR, ELISA and immunostaining. The three cell populations were successfully clustered using supervised and unsupervised methods based on scRNA-seq data. TGF-ß1 led to a fibrotic phenotype in the MTs, detected as decreased albumin and increased αSMA expression. Cell-type specific responses to the treatment were identified for each of the three cell types. They included HepaRG damage characterized by a decrease in cellular metabolism, prototypical inflammatory responses in THP-1s and extracellular matrix remodeling in hTERT-HSCs. Furthermore, we identified novel cell-specific putative fibrosis markers in hTERT-HSC (COL15A1), and THP-1 (ALOX5AP and LAPTM5).

Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences.

SUMMARY: Antibody repertoires reveal insights into the biology of the adaptive immune system and empower diagnostics and therapeutics. There are currently multiple tools available for the annotation of antibody sequences. All downstream analyses such as choosing lead drug candidates depend on the correct annotation of these sequences; however, a thorough comparison of the performance of these tools has not been investigated. Here, we benchmark the performance of commonly used immunoinformatic tools, i.e. IMGT/HighV-QUEST, IgBLAST and MiXCR, in terms of reproducibility of annotation output, accuracy and speed using simulated and experimental high-throughput sequencing datasets.We analyzed changes in IMGT reference germline database in the last 10 years in order to assess the reproducibility of the annotation output. We found that only 73/183 (40%) V, D and J human genes were shared between the reference germline sets used by the tools. We found that the annotation results differed between tools. In terms of alignment accuracy, MiXCR had the highest average frequency of gene mishits, 0.02 mishit frequency and IgBLAST the lowest, 0.004 mishit frequency. Reproducibility in the output of complementarity determining three regions (CDR3 amino acids) ranged from 4.3% to 77.6% with preprocessed data. In addition, run time of the tools was assessed: MiXCR was the fastest tool for number of sequences processed per unit of time. These results indicate that immunoinformatic analyses greatly depend on the choice of bioinformatics tool. Our results support informed decision-making to immunoinformaticians based on repertoire composition and sequencing platforms. AVAILABILITY AND IMPLEMENTATION: All tools utilized in the paper are free for academic use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Traditional and Digital Biomarkers: Two Worlds Apart?

The identification and application of biomarkers in the clinical and medical fields has an enormous impact on society. The increase of digital devices and the rise in popularity of health-related mobile apps has produced a new trove of biomarkers in large, diverse, and complex data. However, the unclear definition of digital biomarkers, population groups, and their intersection with traditional biomarkers hinders their discovery and validation. We have identified current issues in the field of digital biomarkers and put forth suggestions to address them during the DayOne Workshop with participants from academia and industry. We have found similarities and differences between traditional and digital biomarkers in order to synchronize semantics, define unique features, review current regulatory procedures, and describe novel applications that enable precision medicine.

Response of the respiratory mucosal cells to mycobacterium avium subsp. Hominissuis microaggregate.

Mycobacterium avium: subsp. hominissuis (MAH) is an opportunistic pathogen that commonly infects immunocompromised individuals. Recently, we described an invasive phenotypic change MAH undergoes when incubated with lung airway epithelial host cells for 24 h, which is accompanied with microaggregate formation in vitro. The microaggregate phenotype also resulted in higher colonization in the lungs of mice early during infection. Previously, we identified genes highly regulated during microaggregate formation and further characterized the function of two highly upregulated bacterial proteins, mycobacterial binding protein-1 (MBP-1) and mycobacterial inversion protein-1 (MIP-1), which were found to be involved in binding and invasion of the respiratory mucosa. While these studies are valuable in understanding the pathogenesis of MAH, they primarily investigated the bacteria during microaggregate infection without commenting on the differences in the host response to microaggregate and planktonic infection. The bacteria-host interaction between microaggregates and epithelial cells was examined in a variety of assays. Using a transwell polarized epithelial cell model, microaggregates translocated through the monolayer more efficiently than planktonic bacteria at set timepoints. In addition, during infection with microaggregate and planktonic bacteria, host phosphorylated proteins were identified revealing differences in immune response, glutathione synthesis, and apoptosis. The host immune response was further investigated by measuring pro-inflammatory cytokine secretion during microaggregate and planktonic infection of BEAS-2B bronchial epithelial cells. The epithelial cells secreted more CCL5 during infection with microaggregates suggesting that this chemokine may play an important role during microaggregate invasion. Subsequent experiments showed that microaggregates are formed more efficiently in the presence of CCL5, suggesting that MAH had evolved a strategy to use the host response in its benefit. Collectively, this study establishes the different nature of infection by planktonic bacteria and microaggregates.

Identification and verification of hybridoma-derived monoclonal antibody variable region sequences using recombinant DNA technology and mass spectrometry.

Antibody engineering requires the identification of antigen binding domains or variable regions (VR) unique to each antibody. It is the VR that define the unique antigen binding properties and proper sequence identification is essential for functional evaluation and performance of recombinant antibodies (rAb). This determination can be achieved by sequence analysis of immunoglobulin (Ig) transcripts obtained from a monoclonal antibody (MAb) producing hybridoma and subsequent expression of a rAb. However the polyploidy nature of a hybridoma cell often results in the added expression of aberrant immunoglobulin-like transcripts or even production of anomalous antibodies which can confound production of rAb. An incorrect VR sequence will result in a non-functional rAb and de novo assembly of Ig primary structure without a sequence map is challenging. To address these problems, we have developed a methodology which combines: 1) selective PCR amplification of VR from both the heavy and light chain IgG from hybridoma, 2) molecular cloning and DNA sequence analysis and 3) tandem mass spectrometry (MS/MS) on enzyme digests obtained from the purified IgG. Peptide analysis proceeds by evaluating coverage of the predicted primary protein sequence provided by the initial DNA maps for the VR. This methodology serves to both identify and verify the primary structure of the MAb VR for production as rAb.

Mycobacterium tuberculosis Proteome Response to Antituberculosis Compounds Reveals Metabolic "Escape" Pathways That Prolong Bacterial Survival.

Tuberculosis (TB) continues to be one of the most common bacterial infectious diseases and is the leading cause of death in many parts of the world. A major limitation of TB therapy is slow killing of the infecting organism, increasing the risk for the development of a tolerance phenotype and drug resistance. Studies indicate that Mycobacterium tuberculosis takes several days to be killed upon treatment with lethal concentrations of antibiotics both in vitro and in vivo To investigate how metabolic remodeling can enable transient bacterial survival during exposure to bactericidal concentrations of compounds, M. tuberculosis strain H37Rv was exposed to twice the MIC of isoniazid, rifampin, moxifloxacin, mefloquine, or bedaquiline for 24 h, 48 h, 4 days, and 6 days, and the bacterial proteomic response was analyzed using quantitative shotgun mass spectrometry. Numerous sets of de novo bacterial proteins were identified over the 6-day treatment. Network analysis and comparisons between the drug treatment groups revealed several shared sets of predominant proteins and enzymes simultaneously belonging to a number of diverse pathways. Overexpression of some of these proteins in the nonpathogenic Mycobacterium smegmatis extended bacterial survival upon exposure to bactericidal concentrations of antimicrobials, and inactivation of some proteins in M. tuberculosis prevented the pathogen from escaping the fast killing in vitro and in macrophages, as well. Our biology-driven approach identified promising bacterial metabolic pathways and enzymes that might be targeted by novel drugs to reduce the length of tuberculosis therapy.

Identification of Mycobacterium avium subsp. hominissuis secreted proteins using an in vitro system mimicking the phagosomal environment.

BACKGROUND: Mycobacterium avium subsp. hominissuis is a common intracellular pathogen that infects patients with HIV/AIDS and cause lung infection in patients with underlying lung pathology. M.avium preferably infects macrophages and uses diverse mechanisms to alter phagosome maturation. Once in the macrophage, the pathogen can alter the host cellular defenses by secreting proteins into the cytosol of host cells, but despite considerable research, only a few secreted effector proteins have been identified. We hypothesized that the environmental cues inside the phagosome can trigger bacterial protein secretion. To identify M. avium secretome within the phagosome, we utilized a previously established in vitro system that mimics the metal ion concentrations and pH of the M. avium phagosome. RESULTS: M. avium was exposed to phagosome metal concentrations for different time points and exported proteins were profiled and analyzed against bacterial proteins secreted in the culture medium. Mass spectrometric analysis of the secreted proteome identified several proteins, of which 46 were unique to bacteria incubated in the metal mixture. Ten of potential effectors were selected and secretion of these proteins was monitored within M. avium infected mononuclear phagocytic cells using the beta-lactamase FRET-based reporter system. In addition, pull-down assay was performed for secreted calmodulin-like protein MAV_1356 protein to evaluate for eukaryotic target. All examined M. avium proteins were secreted into the macrophage cytosol, and gene expression analysis suggested that the metal environment likely stimulates secretion of pre-made proteins. Further investigation of bacterial secreted MAV_1356 protein, lead to the observation that the MAV_1356 interacts with the host proteins Annexin A1 and Protein S100-A8. CONCLUSIONS: We established an in vitro system for the study if proteins secreted intracellularly, and revealed that the metal mixture mimicking the concentration of metals in the phagosome environment, triggers protein secretion.

Rapid Microfluidic Assay for the Detection of Botulinum Neurotoxin in Animal Sera.

Potent Botulinum neurotoxins (BoNTs) represent a threat to public health and safety. Botulism is a disease caused by BoNT intoxication that results in muscle paralysis that can be fatal. Sensitive assays capable of detecting BoNTs from different substrates and settings are essential to limit foodborne contamination and morbidity. In this report, we describe a rapid 96-well microfluidic double sandwich immunoassay for the sensitive detection of BoNT-A from animal sera. This BoNT microfluidic assay requires only 5 µL of serum, provides results in 75 min using a standard fluorescence microplate reader and generates minimal hazardous waste. The assay has a <30 pg·mL(-1) limit of detection (LOD) of BoNT-A from spiked human serum. This sensitive microfluidic BoNT-A assay offers a fast and simplified workflow suitable for the detection of BoNT-A from serum samples of limited volume in most laboratory settings.

Microaggregate-associated protein involved in invasion of epithelial cells by Mycobacterium avium subsp. hominissuis.

The environmental opportunistic pathogen Mycobacterium avium subsp hominissuis (MAH), a member of the nontuberculous mycobacteria (NTM) cluster, causes respiratory as well as disseminated disease in patients such as those with chronic respiratory illnesses or AIDS. Currently, there is no effective method to prevent NTM respiratory infections. The formation of mycobacterial microaggregates comprises of phenotypic changes that lead to efficient adherence and invasion of the respiratory mucosa in vitro and in vivo. Microaggregate adhesion to the respiratory epithelium is mediated in part through the mycobacterial protein, MAV_3013 (MBP-1). Through DNA microarray analysis, the small hypothetical gene MAV_0831 (Microaggregate Invasion Protein-1, MIP-1) was identified as being upregulated during microaggregate formation. When MIP-1 was overexpressed in poorly-invasive Mycobacterium smegmatis, it provided the bacterium the ability to bind and enter epithelial cells. In addition, incubating microaggregates with recombinant MIP-1 protein enhanced the ability of microaggregates to invade HEp-2 cells, and exposure to anti-MIP-1 immune serum reduced the invasion of the host epithelium. Through protein-protein interaction assays, MIP-1 was found to bind to the host protein filamin A, a cytoskeletal actin-binding protein integral to the modulation of host cell shape and migration. As visualized by immunofluorescence, filamin A was able to co-localize with microaggregates and to a lesser extent planktonic bacteria. Invasion of HEp-2 cells by microaggregates and planktonic bacteria was also inhibited by the addition of anti-filamin A antibody suggesting that filamin A plays an important role during infection. In addition, at earlier time points binding and invasion assay results suggest that MBP-1 participates significantly during the first interactions with the host cell while MIP-1 becomes important once the bacteria adhere to the host epithelium. In summary, we have unveiled one more step associated with MAH crossing the respiratory mucosa.

Mycobacterium avium Possesses Extracellular DNA that Contributes to Biofilm Formation, Structural Integrity, and Tolerance to Antibiotics.

Mycobacterium avium subsp. hominissuis is an opportunistic pathogen that is associated with biofilm-related infections of the respiratory tract and is difficult to treat. In recent years, extracellular DNA (eDNA) has been found to be a major component of bacterial biofilms, including many pathogens involved in biofilm-associated infections. To date, eDNA has not been described as a component of mycobacterial biofilms. In this study, we identified and characterized eDNA in a high biofilm-producing strain of Mycobacterium avium subsp. hominissuis (MAH). In addition, we surveyed for presence of eDNA in various MAH strains and other nontuberculous mycobacteria. Biofilms of MAH A5 (high biofilm-producing strain) and MAH 104 (reference strain) were established at 22°C and 37°C on abiotic surfaces. Acellular biofilm matrix and supernatant from MAH A5 7 day-old biofilms both possess abundant eDNA, however very little eDNA was found in MAH 104 biofilms. A survey of MAH clinical isolates and other clinically relevant nontuberculous mycobacterial species revealed many species and strains that also produce eDNA. RAPD analysis demonstrated that eDNA resembles genomic DNA. Treatment with DNase I reduced the biomass of MAH A5 biofilms when added upon biofilm formation or to an already established biofilm both on abiotic surfaces and on top of human pharyngeal epithelial cells. Furthermore, co-treatment of an established biofilm with DNase 1 and either moxifloxacin or clarithromycin significantly increased the susceptibility of the bacteria within the biofilm to these clinically used antimicrobials. Collectively, our results describe an additional matrix component of mycobacterial biofilms and a potential new target to help treat biofilm-associated nontuberculous mycobacterial infections.

Presence of Virulence-Associated Genes and Ability to Form Biofilm among Clinical Isolates of Escherichia coli Causing Urinary Infection in Domestic Animals.

BACKGROUND: Urinary tract infection caused by Escherichia coli is a frequently observed condition both in humans and animals. Uropathogenic E. coli (UPEC) has been shown to have a pathogenicity island that enables them to infect the urinary tract. Because there is little information about the presence of UPEC-associated virulent genes in animal isolates this work was carried out with the intent to enhance the understanding about the strains of E.coli that cause infections in animals. RESULTS: We screened 21 E. coli strains isolated causing urinary tract infection in domestic animals. Primers were designed to amplify urinary infection-associated genes. Nine genes, papA, tcpC, fyuA, tpbA, Lma, hylA, picU, tonB, and flicC were then amplified and sequenced. Different from the human isolate CFT073, all the animals E. coli lack some of the pathogenesis-associated genes. Genes encoding for proteins used to scavenge iron appear not to be so necessary during animal infections as they are in human infection. In further investigation of phenotypic properties, it was observed that animal UPECs have significantly more impaired ability to form biofilms than human UPEC strain. CONCLUSIONS: This study identified significant differences between human and animal UPECs. This may have its roots in the fact that it is difficult to determine if an animal has symptoms. Future studies will focus on some of the observations.