Bioinformatics Clustering Research Articles

Challenges and opportunities of developing bioinformatics platforms in Africa: the case of BurkinaBioinfo at Joseph Ki-Zerbo University, Burkina Faso.

Bioinformatics, an interdisciplinary field combining biology and computer science, enables meaningful information to be extracted from complex biological data. The exponential growth of biological data, driven by high-throughput omics technologies and advanced sequencing methods, requires robust computational resources. Worldwide, bioinformatics skills and computational clusters are essential for managing and analysing large-scale biological datasets across health, agriculture, and environmental science, which are crucial for the African continent. In Burkina Faso, the establishment of bioinformatics infrastructure has been a gradual process. Initial training initiatives between 2015-2016, including bioinformatics courses and the establishment of the BurkinaBioinfo (BBi) platform, marked significant progress. Over 250 scientists have been trained at diverse levels in bioinformatics, 105 user accounts have been created for high-performance computing access. Operational since 2019, this platform has significantly facilitated training programs for scientists and system administrators in west Africa, covering data production, introductory bioinformatics, phylogenetic analysis, and metagenomics. Financial and technical support from various sources has facilitated the rapid development of the platform to meet the growing need for bioinformatics analysis, particularly in conjunction with local 'wet labs'. Establishing a bioinformatics cluster in Burkina Faso involved identifying the needs of researchers, selecting appropriate hardware and installing the necessary bioinformatics tools. At present, the main challenges for the BBi platform include ongoing staff training in bioinformatics skills and high-level IT infrastructure management in the face of growing infrastructure demands. Despite these challenges, the establishment of a bioinformatics platform in Burkina Faso offers significant opportunities for scientific research and economic development in the country.

From the establishment of a national bioinformatics society to the development of a national bioinformatics infrastructure

We describe the evolution of a bioinformatics national capacity from scattered professionals into a collaborative organisation, and advancements in the adoption of the bioinformatics infrastructure philosophy by the national community. The Romanian Society of Bioinformatics (RSBI), a national professional society, was founded in 2019 to accelerate the development of Romanian bioinformatics. Incrementally, RSBI expanded its role to include: i) developing a community and engaging the public and stakeholders, ii) a national training approach, including through increased interactions with European training resources, and iii) advocating national participation in European bioinformatics infrastructures. In a next step RSBI led the development of the national bioinformatics infrastructure, the Romanian Bioinformatics Cluster (CRB) with the mission to act as an ELIXIR National Node. In this paper we report both the successful projects in training, public engagement, and policy projects, as well as initiatives related to data federation that, while not successful, can serve as valuable learning experiences for future implementations. We explain CRB’s structure and the role such an entity can play in the national bioinformatics infrastructure for data, tools, and training. Finally, we offer insights into the evolving role of the bioinformatics professional society and the synergies and interactions with the National ELIXIR Node

A structural classification of the variant surface glycoproteins of the African trypanosome.

Long-term immune evasion by the African trypanosome is achieved through repetitive cycles of surface protein replacement with antigenically distinct versions of the dense Variant Surface Glycoprotein (VSG) coat. Thousands of VSG genes and pseudo-genes exist in the parasite genome that, together with genetic recombination mechanisms, allow for essentially unlimited immune escape from the adaptive immune system of the host. The diversity space of the "VSGnome" at the protein level was thought to be limited to a few related folds whose structures were determined more than 30 years ago. However, recent progress has shown that the VSGs possess significantly more architectural variation than had been appreciated. Here we combine experimental X-ray crystallography (presenting structures of N-terminal domains of coat proteins VSG11, VSG21, VSG545, VSG558, and VSG615) with deep-learning prediction using Alphafold to produce models of hundreds of VSG proteins. We classify the VSGnome into groups based on protein architecture and oligomerization state, contextualize recent bioinformatics clustering schemes, and extensively map VSG-diversity space. We demonstrate that in addition to the structural variability and post-translational modifications observed thus far, VSGs are also characterized by variations in oligomerization state and possess inherent flexibility and alternative conformations, lending additional variability to what is exposed to the immune system. Finally, these additional experimental structures and the hundreds of Alphafold predictions confirm that the molecular surfaces of the VSGs remain distinct from variant to variant, supporting the hypothesis that protein surface diversity is central to the process of antigenic variation used by this organism during infection.

GLCC: A General Framework for Graph-Level Clustering

This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent years have witnessed the success of deep clustering coupled with graph neural networks (GNNs). However, existing methods focus on clustering among nodes given a single graph, while exploring clustering on multiple graphs is still under-explored. In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs. Specifically, GLCC first constructs an adaptive affinity graph to explore instance- and cluster-level contrastive learning (CL). Instance-level CL leverages graph Laplacian based contrastive loss to learn clustering-friendly representations while cluster-level CL captures discriminative cluster representations incorporating neighbor information of each sample. Moreover, we utilize neighbor-aware pseudo-labels to reward the optimization of representation learning. The two steps can be alternatively trained to collaborate and benefit each other. Experiments on a range of well-known datasets demonstrate the superiority of our proposed GLCC over competitive baselines.

Re-definition of the DR3, DR4, DR9 and DR11 motifs by cluster analysis allows the identification of the hidden DR52 and DR53 peptide repertoires

HLA-DR alleles are commonly inherited as haplotypes. While HLA-DR3 (DRB1*0301) and -DR11 (DRB1*1101) are linked with different alleles of HLA-DR52 (DRB3), HLA-DR4 (DRB1*0401) and -DR9 (DRB1*0901) are linked with HLA-DR53 (DRB4). The overexpression of the DRB1-encoded molecules on the cell surface has hindered the definition of the peptide binding motifs for the non-DRB1 molecules because of the difficulty to distinguish the peptides corresponding to each isotype. The definition of these hidden motifs is of vital importance because of the recently description in bibliography of cancer or autoimmunity related peptides supposedly presented by these non-DRB1 molecules. Here, we show an innovative bioinformatics clustering analysis that allows such discrimination. We isolated class II-peptide complexes from HLA-DR homozygous B cell lines by immunoprecipitation with an HLA-DR-specific antibody and we identified the binding peptides by mass spectrometry. Later, the eluted peptides were analysed with the GibbsCluster-2.0 software. This workflow let us to differentiate peptides that belong to DR3 or DR11 from those of the DR52 isotype, as well as the ones from DR4 or DR9 and DR53. Our results confirmed the DR3 and DR4 motifs and improved the accuracy of the, since this moment, incompletely defined DR9 and DR11 motifs. Moreover, we were able to establish the motifs corresponding to HLA-DR52 alleles, in addition to HLA-DR53. Surprisingly, we have found a distribution of the conserved positions in the non-DRB1 motifs (P1, P7 and P9) different from the typically found in DRB1 molecules (P1, P4/P6 and P9), highlighting a proline and an aspartic acid in P7 of the HLA-DR52 and HLA-DR53 molecules, respectively. This phenomenon is absolutely uncommon in DRB1 alleles. In conclusion, we have developed a new strategy to define hidden HLA-DR motifs that could enable to show up a novel landscape of target peptides capable of being used in immunotherapy.

Inhibitory Response to CK II Inhibitor Silmitasertib and CDKs Inhibitor Dinaciclib Is Related to Genetic Differences in Pancreatic Ductal Adenocarcinoma Cell Lines.

Casein kinase II (CK2) and cyclin-dependent kinases (CDKs) frequently interact within multiple pathways in pancreatic ductal adenocarcinoma (PDAC). Application of CK2- and CDK-inhibitors have been considered as a therapeutic option, but are currently not part of routine chemotherapy regimens. We investigated ten PDAC cell lines exposed to increasing concentrations of silmitasertib and dinaciclib. Cell proliferation, metabolic activity, biomass, and apoptosis/necrosis were evaluated, and bioinformatic clustering was used to classify cell lines into sensitive groups based on their response to inhibitors. Furthermore, whole exome sequencing (WES) and RNA sequencing (RNA-Seq) was conducted to assess recurrent mutations and the expression profile of inhibitor targets and genes frequently mutated in PDAC, respectively. Dinaciclib and silmitasertib demonstrated pronounced and limited cell line specific effects in cell death induction, respectively. WES revealed no genomic variants causing changes in the primary structure of the corresponding inhibitor target proteins. RNA-Seq demonstrated that the expression of all inhibitor target genes was higher in the PDAC cell lines compared to non-neoplastic pancreatic tissue. The observed differences in PDAC cell line sensitivity to silmitasertib or dinaciclib did not depend on target gene expression or the identified gene variants. For the PDAC hotspot genes kirsten rat sarcoma virus (KRAS) and tumor protein p53 (TP53), three and eight variants were identified, respectively. In conclusion, both inhibitors demonstrated in vitro efficacy on the PDAC cell lines. However, aberrations and expression of inhibitor target genes did not appear to affect the efficacy of the corresponding inhibitors. In addition, specific aberrations in TP53 and KRAS affected the efficacy of both inhibitors.

Transcriptional Profiling of Monocytes Deficient in Nuclear Orphan Receptors NR4A2 and NR4A3 Reveals Distinct Signalling Roles Related to Antigen Presentation and Viral Response.

The nuclear receptor sub-family 4 group A (NR4A) family are early response genes that encode proteins that are activated in several tissues/cells in response to a variety of stressors. The NR4A family comprises NR4A1, NR4A2 and NR4A3 of which NR4A2 and NR4A3 are under researched and less understood, particularly in the context of immune cells. NR4A expression is associated with multiple diseases e.g. arthritis and atherosclerosis and the development of NR4A-targetting molecules as therapeutics is a current focus in this research field. Here, we use a combination of RNA-sequencing coupled with strategic bioinformatic analysis to investigate the down-stream effects of NR4A2 and NR4A3 in monocytes and dissect their common and distinct signalling roles. Our data reveals that NR4A2 and NR4A3 depletion has a robust and broad-reaching effect on transcription in both the unstimulated state and in the presence of LPS. Interestingly, many of the genes affected were present in both the unstimulated and stimulated states revealing a previously unappreciated role for the NR4As in unstimulated cells. Strategic clustering and bioinformatic analysis identified both distinct and common transcriptional roles for NR4A2 and NR4A3 in monocytes. NR4A2 notably was linked by both bioinformatic clustering analysis and transcription factor interactome analysis to pathways associated with antigen presentation and regulation of MHC genes. NR4A3 in contrast was more closely linked to pathways associated with viral response. Functional studies further support our data analysis pointing towards preferential/selective roles for NR4A2 in the regulation of antigen processing with common roles for NR4A2 and NR4A3 evident with respect to cell migration. Taken together this study provides novel mechanistic insights into the role of the enigmatic nuclear receptors NR4A2 and NR4A3 in monocytes.

Identification of the molecular determinants driving the substrate specificity of fungal lytic polysaccharide monooxygenases (LPMOs)

Understanding enzymatic breakdown of plant biomass is crucial to develop nature-inspired biotechnological processes. Lytic polysaccharide monooxygenases (LPMOs) are microbial enzymes secreted by fungal saprotrophs involved in carbon recycling. LPMOs modify biomass by oxidatively cleaving polysaccharides, thereby enhancing the efficiency of glycoside hydrolases. Fungal AA9 LPMOs are active on cellulose, but some members also display activity on hemicelluloses and/or oligosaccharides. Although the active site subsites are well defined for a few model LPMOs, the molecular determinants driving broad substrate specificity are still not easily predictable. Based on bioinformatic clustering and sequence alignments, we selected seven fungal AA9 LPMOs that differ in the amino-acid residues constituting their subsites. Investigation of their substrate specificities revealed that all these LPMOs are active on cellulose and cello-oligosaccharides, as well as plant cell wall–derived hemicellulosic polysaccharides, and carry out C4 oxidative cleavage. The product profiles from cello-oligosaccharide degradation suggest that the subtle differences in amino-acid sequence within the substrate-binding loop regions lead to different preferred binding modes. Our functional analyses allowed us to probe the molecular determinants of substrate binding within two AA9 LPMO subclusters. Many wood-degrading fungal species rich in AA9 genes have at least one AA9 enzyme with structural loop features that allow recognition of short β-(1,4)–linked glucan chains. Time-course monitoring of these AA9 LPMOs on cello-oligosaccharides also provides a useful model system for mechanistic studies of LPMO catalysis. These results are valuable for the understanding of LPMO contribution to wood decaying process in nature and for the development of sustainable biorefineries.

Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment

Bioinformatics clustering application for mining of a large set of olive pollen aerobiological data to describe the daily distribution of Olea pollen concentration. The study was performed with hourly pollen concentrations measured during 8 years (2011–2018) in Extremadura (Spain). Olea pollen season by quartiles of the pollen integral in preseason (Q1: 0%–25%), in-season (Q2 and Q3: 25%–75%) and postseason (Q4: 75%–100%). Days with pollen concentrations above 100 grains/m3 were clustered according to the daily distribution of the concentrations. The factors affecting the prevalence of the different clusters were analyzed: distance to olive groves and the moment during the pollen season and the meteorology. During the season, the highest hourly concentrations during the day where between 12:00 and 14:00, while during the preseason the highest hourly concentrations were detected in the afternoon and evening hours. In the postseason the pollen concentrations were more homogeneously distributed during 9-16 h. The representation shows a well-defined hourly pattern during the season, but a more heterogeneous distribution during the preseason and postseason. The cluster dendrogram shows that all the days could be clustered in 6 groups: most of the clusters shows the daily peaks between 11:00 and 15:00 with a smooth curve (Cluster 1 and 3) or with a strong peak (2 and 5). Days included in cluster 9 shows an earlier peak in the morning (before 9:00). On the other hand, cluster 6 shows a peak in the afternoon, after 15:00. Hourly concentrations show a sharper pattern during the season, with the peak during the hours close to the emission. Out of the season, when pollen is expected to come from farther distances, the hourly peak is located later from the emission time of the trees. Significant factors for predicting the hourly pattern were wind speed and direction and the distance to the olive groves.

Adaptive computation of the Symmetric Nonnegative Matrix Factorization (SymNMF)

Symmetric Nonnegative Matrix Factorization (SymNMF) provides a symmetric nonnegative low rank decomposition of symmetric matrices. It has found application in several domains such as data mining, bioinformatics and graph clustering. In this paper SymNMF is obtained by solving a penalized nonsymmetric minimization problem. Instead of letting the penalizing parameter increase according to an a priori fixed rule, as suggested in literature, we propose a heuristic approach based on an adaptive technique. The factorization is performed by applying, as inner-outer procedure, the Alternating Nonnegative Least Squares (ANLS). The inner problems are solved by two nonnegative quadratic optimization methods: an Active-Set-like method (BPP) and the Greedy Coordinate Descent method (GCD). Extensive experimentation shows that the proposed heuristic approach is effective with both the inner methods, GCD outperforming BPP in terms of computational time complexity.

Abstract 827: Single-cell Transcriptomic Profiling Provides Insights Into Disease-related Processes During Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a genetic disease usually caused by a heterozygous mutation in a sarcomeric gene. Histologically, HCM is characterized by a heterogeneous population of hypertrophic, disorganized cardiomyocytes and fibrosis of heart tissue. This hypertrophic growth can lead to outflow tract obstruction which can ultimately lead to progressive heart failure due to cardiac overload. Here we use single-cell RNA sequencing (scRNA-Seq) on septal myectomy samples from patients suffering from HCM to interrogate cellular heterogeneity in disease context. Using our previously optimized digestion and sorting protocol we were able to obtain good quality RNA for single cell analysis. Bioinformatic clustering analysis of single cell transcriptomes revealed 8 transcriptomically distinct subpopulations of cardiomyocytes. Our data indicate an NPPA/NPPB enriched cluster of cells and show TTN to function as an important determinant of the cellular heterogeneity. Correlation analysis links TTN expression to the expression of, among others, cardiomyopathy-associated protein (CMYA) 5 and 3, while there appears to be a negative correlation with, among others, TMEM212. We were able to confirm these correlations by RT-PCR on bulk RNA in an additional cohort of 97 HCM myectomy samples. Additionally, immuno-histochemical staining on myectomy samples and explanted HCM hearts confirmed the transcriptional heterogeneity among cardiomyocytes. A unique advantage of FACS-based single cell sorting cardiomyocytes is the availability of index data for the sorted cells. Correlation analysis of forward scatter, as a proxy for cell size, confirmed both well-known stress markers MYL2 and MYL7 and additional genes to be upregulated in the larger cells. In summary, we show that FACS-based sorting of HCM cardiomyocytes allows for scRNA-Seq of intact cardiomyocytes. Initial analyses of the data could be validated in a wider cohort of comparable samples, showing reliability of the data. Finally, incorporation of index data reveals genes correlated with cell size. Further interrogation of this data has the potential to reveal novel insights into the pathogenesis of HCM.

Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings

Next-generation sequencing (NGS) technologies have revolutionized biological research by generating genomic data that were once unaffordable by traditional first-generation sequencing technologies. These sequencing methodologies provide an opportunity for in-depth analyses of host and pathogen genomes as they are able to sequence millions of templates at a time. However, these large datasets can only be efficiently explored using bioinformatics analyses requiring huge data storage and computational resources adapted for high-performance processing. High-performance computing allows for efficient handling of large data and tasks that may require multi-threading and prolonged computational times, which is not feasible with ordinary computers. However, high-performance computing resources are costly and therefore not always readily available in low-income settings. We describe the establishment of an affordable high-performance computing bioinformatics cluster consisting of 3 nodes, constructed using ordinary desktop computers and open-source software including Linux Fedora, SLURM Workload Manager, and the Conda package manager. For the analysis of large antibody sequence datasets and for complex viral phylodynamic analyses, the cluster out-performed desktop computers. This has demonstrated that it is possible to construct high-performance computing capacity capable of analyzing large NGS data from relatively low-cost hardware and entirely free (open-source) software, even in resource-limited settings. Such a cluster design has broad utility beyond bioinformatics to other studies that require high-performance computing.

Sequence clustering in bioinformatics: an empirical study.

Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/.

Quantitative Proteomic Profiling of the Human Ovary from Early to Mid-Gestation Reveals Protein Expression Dynamics of Oogenesis and Folliculogenesis.

The in vivo gene networks involved in coordinating human fetal ovarian development remain obscure. In this study, quantitative mass spectrometry was conducted on ovarian tissue collected at key stages during the first two trimesters of human gestational development, confirming the expression profiling data using immunofluorescence, as well as in vitro modeling with human oogonial stem cells (OSCs) and human embryonic stem cells (ESCs). A total of 3,837 proteins were identified in samples spanning developmental days 47-137. Bioinformatics clustering and Ingenuity Pathway Analysis identified DNA mismatch repair and base excision repair as major pathways upregulated during this time. In addition, MAEL and TEX11, two key meiosis-related proteins, were identified as highly expressed during the developmental window associated with fetal oogenesis. These findings were confirmed and extended using in vitro differentiation of OSCs into in vitro derived oocytes and of ESCs into primordial germ cell-like cells and oocyte-like cells, as models. In conclusion, the global protein expression profiling data generated by this study have provided novel insights into human fetal ovarian development in vivo and will serve as a valuable new resource for future studies of the signaling pathways used to orchestrate human oogenesis and folliculogenesis.

Clustering by fast search and merge of local density peaks for gene expression microarray data

Clustering is an unsupervised approach to classify elements based on their similarity, and it is used to find the intrinsic patterns of data. There are enormous applications of clustering in bioinformatics, pattern recognition, and astronomy. This paper presents a clustering approach based on the idea that density wise single or multiple connected regions make a cluster, in which density maxima point represents the center of the corresponding density region. More precisely, our approach firstly finds the local density regions and subsequently merges the density connected regions to form the meaningful clusters. This idea empowers the clustering procedure, in which outliers are automatically detected, higher dense regions are intuitively determined and merged to form clusters of arbitrary shape, and clusters are identified regardless the dimensionality of space in which they are embedded. Extensive experiments are performed on several complex data sets to analyze and compare our approach with the state-of-the-art clustering methods. In addition, we benchmarked the algorithm on gene expression microarray data sets for cancer subtyping; to distinguish normal tissues from tumor; and to classify multiple tissue data sets.

Weight loss-induced cellular stress in subcutaneous adipose tissue and the risk for weight regain in overweight and obese adults.

Weight loss is often followed by weight regain after the dietary intervention (DI). Cellular stress is increased in adipose tissue of obese individuals. However, the relation between cellular stress and weight regain is unclear. Previously, we observed increased adipose tissue cellular stress of participants regaining weight compared with participants maintaining weight loss. In the current study, we further investigated the relation between weight regain and changes in the expression of stress-related genes and stress protein levels to determine possible predictors of weight regain. In this randomized controlled trial, sixty-one healthy overweight/obese participants followed a DI of either a 5-week very-low-calorie diet (500 kcal per day) or a 12-week low-calorie diet (1250 kcal per day; WL period) with a subsequent 4-week weight stable diet (WS period), and a 9-month follow-up. The WL and WS period taken together was named the DI. Abdominal subcutaneous adipose tissue biopsies were collected in 53 participants for microarray and liquid chromatography-mass spectrometry analysis. RNA and protein levels for a broad set of stress-related genes were correlated to the weight regain percentage. Different gene sets correlated to weight regain percentage during WS and DI. Bioinformatics clustering suggests that during the WS phase-defined genes for actin filament dynamics, glucose handling and nutrient sensing are related to weight regain. HIF-1 (hypoxia-inducible factor-1) is indicated as an important regulator. With regard to DI, clustering of correlated genes indicate that LGALS1, ENO1 and ATF2 are important nodes for conferring risk for weight regain. Our present findings indicate that the risk for weight regain is related to expression changes of distinct sets of stress-related genes during the first 4 weeks after returning to energy balance, and during the DI. Further research is required to investigate the mechanistic significance of these findings and find targets for preventing weight regain.

The active intrathecal B-cell response in LGI1-antibody encephalitis

BackgroundLeucine-rich glioma inactivated 1 (LGI1) is a component of the voltage-gated potassium channel complex. IgG antibodies against LGI1 are associated with immunotherapy-responsive encephalitis and epilepsies. LGI1-antibody concentrations are 10–100 times greater in serum than in cerebrospinal fluid (CSF). Oligoclonal IgG bands are rarely found in patients with LGI1-antibody encephalitis or epilepsy. These observations raise questions about the sources of the B cells that result in production of LGI1 antibodies and how the IgGs reach the brain. We aimed to investigate the migration and expansions of peripheral and central B cells to the production of LGI1-specific IgG. MethodsWe performed PCR amplification and next generation deep immune repertoire sequencing of immunoglobulin (Ig) heavy chain variable regions (VH) from CSF and subsorted peripheral blood B-cell populations from two patients with limbic encephalitis and faciobrachial dystonic seizures associated with LGI1 antibodies. Bioinformatics clustering of related IgM-VH or IgG-VH transcripts was used to determine whether active B-cell diversification could be observed, and whether intrathecal B-cell repertoires, if present, were related to peripheral B cells. FindingsWe identified clusters of related Ig-VH transcripts in the CSF of both patients. Within these clusters there was a range of somatic hypermutations along the IGHV germline segment-derived portion. In addition, we identified a large number of closely related Ig-VH clusters that were common to both CSF and peripheral blood, including a small number of dominating Ig-VH clusters that might represent the most active clonally related B-cell populations. InterpretationOur data suggest that some B-cell affinity maturation occurs inside the CNS compartment in LGI1-antibody encephalitis. Somatic hypermutation rates point to a CSF antigen-driven activation of clonally related B cells that shape the intrathecal immune repertoire. The target antigen or antigens of these clonally related B cells remain unknown; our work continues to determine the relative contribution of intrathecally activated and peripheral LGI1-specific B cells in this autoimmune CNS disease. FundingWellcome Trust Intermediate Fellowship to SRI, Fulbright-MS Society, Epilepsy Research UK, BMA Vera Down Research Grant.

Limbic encephalitis with antibodies to LGI1 is associated with an active intrathecal B cell response

Background: Leucine-rich glioma inactivated 1 (LGI1) is a component of the voltage-gated potassium channel complex (VGKC). Antibodies against LGI1 are associated with limbic encephalitis and related central nervous system (CNS) disorders. In most patients, anti-LGI1 antibodies are found in the serum, while their occurrence in the cerebrospinal fluid (CSF) is much less common. Oligoclonal IgG (oligoclonal bands, OCB), a prominent indicator for intrathecal B cell activation, are rarely found in patients with LGI1 encephalitis. Overall, the immune pathology resulting in LGI1 autoimmunity is unclear. However, partially due to the absence of OCB, peripheral immune processes are believed to be the principal disease drivers. Objective: To investigate whether an active intrathecal B cell immune response is present in patients with LGI1 antibodies. Methods: We applied PCR amplification and next generation deep immune repertoire sequencing (DIRS) of B cell receptor immunoglobulin (Ig) heavy chain variable regions (VH) from CSF and peripheral blood B cells of 2 patients with LGI1 encephalitis. Extensive bioinformatics clustering of related IgM-VH or IgG-VH transcripts was employed to determine 1) whether active B cell immune mechanisms operate in LGI1 encephalitis, and 2) whether intrathecal B cell repertoires, if present, are related to peripheral B cells. Results: We identified numerous clusters of related Ig-VH transcripts in the CSF of both patients; within these clusters we found a range of somatic hypermutations (SHM) along the IGHV germline segmentderived portion suggesting that local antigen-driven activation of clonally related B cells was involved in shaping the intrathecal immune repertoire. Furthermore, we identified a large number of closely related Ig-VH clusters that were common to both, CSF and periphery. Interestingly, in each patient we identified a small number of dominating Ig-VH clusters that may represent the most active clonally related B cell populations. Conclusion: Our data suggest that in LGI1 encephalitis B cell affinity maturation occurs inside the CNS compartment. The target antigen(s) of the clonally related B cells described here remain unknown; ongoing work will determine whether intrathecally activated B cells and/or B cells that apparently target identical antigens in the periphery and CNS compartments are involved in LGI1 autoimmunity.

‘Cytokine defined’ B cell subsets in multiple sclerosis

Background: Leucine-rich glioma inactivated 1 (LGI1) is a component of the voltage-gated potassium channel complex (VGKC). Antibodies against LGI1 are associated with limbic encephalitis and related central nervous system (CNS) disorders. In most patients, anti-LGI1 antibodies are found in the serum, while their occurrence in the cerebrospinal fluid (CSF) is much less common. Oligoclonal IgG (oligoclonal bands, OCB), a prominent indicator for intrathecal B cell activation, are rarely found in patients with LGI1 encephalitis. Overall, the immune pathology resulting in LGI1 autoimmunity is unclear. However, partially due to the absence of OCB, peripheral immune processes are believed to be the principal disease drivers. Objective: To investigate whether an active intrathecal B cell immune response is present in patients with LGI1 antibodies. Methods: We applied PCR amplification and next generation deep immune repertoire sequencing (DIRS) of B cell receptor immunoglobulin (Ig) heavy chain variable regions (VH) from CSF and peripheral blood B cells of 2 patients with LGI1 encephalitis. Extensive bioinformatics clustering of related IgM-VH or IgG-VH transcripts was employed to determine 1) whether active B cell immune mechanisms operate in LGI1 encephalitis, and 2) whether intrathecal B cell repertoires, if present, are related to peripheral B cells. Results: We identified numerous clusters of related Ig-VH transcripts in the CSF of both patients; within these clusters we found a range of somatic hypermutations (SHM) along the IGHV germline segmentderived portion suggesting that local antigen-driven activation of clonally related B cells was involved in shaping the intrathecal immune repertoire. Furthermore, we identified a large number of closely related Ig-VH clusters that were common to both, CSF and periphery. Interestingly, in each patient we identified a small number of dominating Ig-VH clusters that may represent the most active clonally related B cell populations. Conclusion: Our data suggest that in LGI1 encephalitis B cell affinity maturation occurs inside the CNS compartment. The target antigen(s) of the clonally related B cells described here remain unknown; ongoing work will determine whether intrathecally activated B cells and/or B cells that apparently target identical antigens in the periphery and CNS compartments are involved in LGI1 autoimmunity.

Review of clustering in bioinformatics and drug design by John D. MacCuish and Norah E. MacCuish

Review of clustering in bioinformatics and drug design by John D. MacCuish and Norah E. MacCuish