Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data

B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same "clonal family") are released from the germinal center, their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called "substitution profiles", are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method "Substitution Profiles Using Related Families" (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on an external dataset. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models.Comment: 23 page

지도 학습 기반 바이오패닝 클론 증폭 패턴 분석을 통한 항원 결합 반응성 예측

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2021.8. 정준호.Background: Monoclonal antibodies (mAbs) are produced by B cells and specifically binds to target antigens. Technical advances in molecular and cellular cloning made it possible to purify recombinant mAbs in a large scale, enhancing the multiple research area and potential for their clinical application. Since the importance of therapeutic mAbs is increasing, mAbs have become the predominant drug classes for various diseases over the past decades. During that time, immense technological advances have made the discovery and development of mAb therapeutics more efficient. Owing to advances in high-throughput methodology in genomic sequencing, phenotype screening, and computational data analysis, it is conceivable to generate the panel of antibodies with annotated characteristics without experiments. Thesis objective: This thesis aims to develop the next-generation antibody discovery methods utilizing high-throughput antibody repertoire sequencing and bioinformatics analysis. I developed novel methods for construction of in vitro display antibody library, and machine learning based antibody discovery. In chapter 3, I described a new method for generating immunoglobulin (Ig) gene repertoire, which minimizes the amplification bias originated from a large number of primers targeting diverse Ig germline genes. Universal primer-based amplification method was employed in generating Ig gene repertoire then validated by high-throughput antibody repertoire sequencing, in the aspect of clonal diversity and immune repertoire reproducibility. A result of this research work is published in ‘Journal of Immunological Methods (2021). doi: 10.1016/j.jim.2021. 113089’. In chapter 4, I described a novel machine learning based antibody discovery method. In conventional colony screening approach, it is impossible to identify antigen specific binders having low clonal abundance, or hindered by non-specific phage particles having antigen reactivity on p8 coat protein. To overcome the limitations, I applied the supervised learning algorithm on high-throughput sequencing data annotated with binding property and clonal frequency through bio-panning. NGS analysis was performed to generate large number of antibody sequences annotated with its’ clonal frequency at each selection round of the bio-panning. By using random forest (RF) algorithm, antigen reactive binders were predicted and validated with in vitro screening experiment. A result of this research work is published in ‘Experimental & Molecular Medicine (2017). doi:0.1038/emm.2017.22’ and ‘Biomolecule (2020). doi:10.3390/biom10030421’. Conclusion: By combining conventional antibody discovery techniques and high-throughput antibody repertoire sequencing, it was able to make advances in multiple attributes of the previous methodology. Multi-cycle amplification with Ig germline gene specific primers showed the high level of repertoire distortion, but could be improved by employing universal primer-based amplification method. RF model generates the large number of antigen reactive antibody sequences having various clonal enrichment pattern. This result offers the new insight in interpreting clonal enrichment process, frequency of antigen specific binder does not increase gradually but depends on the multiple selection rounds. Supervised learning-based method also provides the more diverse antigen specific clonotypes than conventional antibody discovery methods.연구의 배경: 단일 클론 항체 (monoclonal antibody, mAb) 는 B 세포에서 생산되어 표적 항원에 특이적으로 결합하는 폴리펩타이드 복합체 이다. 분자 및 세포 클로닝 기술의 발전으로 재조합 단일 클론 항체를 대용량으로 생산하는것이 가능해졌으며, 이를 바탕으로 다양한 연구 및 임상 분야에서의 활용이 확대되고 있다. 또한 치료용 항체를 효율적으로 발굴하고 개발하는 기술에 대한 비약적인 발전이 이루어졌다. 유전자 서열 분석, 표현형 스크리닝, 컴퓨팅 기반 분석법 분야에서 이루어진 고집적 방법론 (high-throughput methodology) 의 발전과 이의 응용을 통해, 비실험적 방법을 통해 항원 반응성 항체 패널을 생산하는것이 가능해졌다. 연구의 목표: 본 박사 학위 논문은 고집적 항체 레퍼토어 시퀀싱 (high-throughput antibody repertoire sequencing) 과 생물정보학 (bioinformatics) 기법을 활용하여 신규한 (novel) 차세대 항체 발굴법 (next-generation antibody discovery method) 을 개발하는것을 목표로 하고 있다. 본 연구를 통해 in vitro display 항체 라이브러리를 제작하기 위한 신규 프로토콜 및 기계 학습을 기반으로한 항체 발굴법을 개발 하였다. Chapter 3: 항체 레퍼토어를 증폭하는 과정에서, 다수의 생식세포 면역 글로불린 유전자 (germline immunoglobulin gene) 특이적 프라이머 사용에 의해 발생하는 증폭 편차 (amplification bias) 를 최소화 하는 방법론에 대해 기술하였다. 유니버셜 (universal) 프라이머를 사용한 다중 사이클 증폭 (multi-cycle amplification) 법이 사용되었으며, 고집적 항체 레퍼토어 시퀀싱을 통해, 클론 다양성 (clonal diversity) 및 면역 레퍼토어 재구성도 (immune repertoire reproducibility) 를 생물정보학적 기법으로 측정하여 신규 방법론에 대한 검증을 수행하였다. 본 연구의 연구결과는 다음의 학술지에 출판 되었다: Journal of Immunological Methods (2021). doi: 10.1016/j.jim.2021. 113089. Chapter 4: 기계 학습 기반의 항체 발굴법 개발에 대해 기술하였다. 전통적 콜로니 스크리닝 (colony screening) 방법에서는, 클론 빈도 (clonal abundance) 가 낮은 클론을 발굴 하거나 선택압 (selective pressure) 이 부여되는 과정에서, p8 표면 단백질의 비 특이적 항원 특이성을 제거할 수 없다. 이러한 제한점을 극복하기 위해서 항원 결합능 및 바이오패닝 에서의 클론 빈도가 측정 되어있는 고집적 항체 서열 데이터를 대상으로 지도 학습 알고리즘을 적용하였다. 랜덤 포레스트 (random forest, RF) 알고리즘을 적용하여 항원 특이적 항체 클론을 예측하였으며, 시험관 내 스크리닝을 통해 항원 특이성을 검증하였다. 본 연구의 연구 결과는 다음의 학술지에 출판되었다: 1) Experimental & Molecular Medicine (2017). doi:0.1038/emm.2017.22., 2) Biomolecule (2020). doi:10.3390/biom10030421. 결론: 전통적 항체 발굴 기술과 고집적 항체 레퍼토어 시퀀싱 기술을 융합함으로써, 기존 방법론의 다양한 한계점을 개선할 수 있었다. 면역 글로불린 생식세포 유전자 특이적 프라이머를 사용한 다중 사이클 증폭은 클론 빈도 및 다양성에 왜곡을 유도 하였으나, 유니버셜 프라이머를 사용한 증폭법을 통해 높은 효율로 레퍼토어 왜곡을 개선시킬 수 있음을 관찰할 수 있었다. RF 모델은 다양한 클론 증폭 패턴 (enrichment pattern) 을 가지는 항원 반응성 항체 서열을 생성하였다. 이를 통해 항원에 특이적으로 결합하는 클론이 단계적으로 증폭되는 것이 아니라 초기 및 후기의 다수의 선별 단계 (selection round) 에 의존함을 확인할 수 있었으며, 바이오패닝 에서의 클론 증폭에 대한 새로운 해석을 제시하였다. 또한 지도 학습을 기반으로 발굴 된 클론들에서, 전통적 콜로니 스크리닝 방법과 대비하여 더 높은 서열 다양성을 관찰할 수 있었다.1. Introduction 8 1.1. Antibody and immunoglobulin repertoire 8 1.2. Antibody therapeutics 16 1.3. Methodology: antibody discovery and engineering 21 2. Thesis objective 28 3. Establishment of minimally biased phage display library construction method for antibody discovery 29 3.1. Abstract 29 3.2. Introduction 30 3.3. Results 32 3.4. Discussion 44 3.5. Methods 47 4. In silico identification of target specific antibodies by high-throughput antibody repertoire sequencing and machine learning 58 4.1. Abstract 58 4.2. Introduction 60 4.3. Results 64 4.4. Discussion 111 4.5. Methods 116 5. Future perspectives 129 6. References 135 7. Abstract in Korean 150박

Genome analysis of a highly virulent serotype 1 strain of streptococcus pneumoniae from West Africa

Streptococcus pneumoniae is a leading cause of pneumonia, meningitis, and bacteremia, estimated to cause 2 million deaths annually. The majority of pneumococcal mortality occurs in developing countries, with serotype 1 a leading cause in these areas. To begin to better understand the larger impact that serotype 1 strains have in developing countries, we characterized virulence and genetic content of PNI0373, a serotype 1 strain from a diseased patient in The Gambia. PNI0373 and another African serotype 1 strain showed high virulence in a mouse intraperitoneal challenge model, with 20% survival at a dose of 1 cfu. The PNI0373 genome sequence was similar in structure to other pneumococci, with the exception of a 100 kb inversion. PNI0373 showed only15 lineage specific CDS when compared to the pan-genome of pneumococcus. However analysis of non-core orthologs of pneumococcal genomes, showed serotype 1 strains to be closely related. Three regions were found to be serotype 1 associated and likely products of horizontal gene transfer. A detailed inventory of known virulence factors showed that some functions associated with colonization were absent, consistent with the observation that carriage of this highly virulent serotype is unusual. The African serotype 1 strains thus appear to be closely related to each other and different from other pneumococci despite similar genetic content

Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Background Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Results Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. Conclusions The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis