Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4+ cells (Th1 and Th2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 Th cells (Th1 and Th17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).

Molecular signature of methotrexate response among rheumatoid arthritis patients.

Background: Methotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but failure of satisfying treatment response occurs in a significant proportion of patients. Here we present a longitudinal multi-omics study aimed at detecting molecular and cellular processes in peripheral blood associated with a successful methotrexate treatment of rheumatoid arthritis. Methods: Eighty newly diagnosed patients with RA underwent clinical assessment and donated blood before initiation of MTX, and 3 months into treatment. Flow cytometry was used to describe cell types and presence of activation markers in peripheral blood, the expression of 51 proteins was measured in serum or plasma, and RNA sequencing was performed in peripheral blood mononuclear cells (PBMC). Response to treatment after 3 months was determined using the EULAR response criteria. We assessed the changes in biological phenotypes during treatment, and whether these changes differed between responders and non-responders with regression analysis. By using measurements from baseline, we also tried to find biomarkers of future MTX response or, alternatively, to predict MTX response. Results: Among the MTX responders, (Good or Moderate according to EULAR treatment response classification, n = 60, 75%), we observed changes in 29 partly overlapping cell types proportions, levels of 13 proteins and expression of 38 genes during treatment. These changes were in most cases suppressions that were stronger among responders compared to non-responders. Within responders to treatment, we observed a suppression of FOXP3 gene expression, reduction of immunoglobulin gene expression and suppression of genes involved in cell proliferation. The proportion of many HLA-DR expressing T-cell populations were suppressed in all patients irrespective of clinical response, and the proportion of many IL21R+ T-cells were reduced exclusively in non-responders. Using only the baseline measurements we could not detect any biomarkers or prediction models that could predict response to MTX. Conclusion: We conclude that a deep molecular and cellular phenotyping of peripheral blood cells in RA patients treated with methotrexate can reveal previously not recognized differences between responders and non-responders during 3 months of treatment with MTX. This may contribute to the understanding of MTX mode of action and explain non-responsiveness to MTX therapy.

Identification of proteinase 3 autoreactive CD4+T cells and their T-cell receptor repertoires in antineutrophil cytoplasmic antibody-associated vasculitis.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is an autoimmune disease involving autoreactivity to proteinase 3 (PR3) as demonstrated by presence of ANCAs. While autoantibodies are screened for diagnosis, autoreactive T cells and their features are less well-studied. Here, we investigated PR3-specific CD4+T cell responses and features of autoreactive T cells in patients with PR3-AAV, using a cohort of 72 patients with either active or inactive disease. Autoreactive PR3-specific CD4+T cells producing interferon γ in response to protein stimulation were found to express the G-protein coupled receptor 56 (GPR56), a cell surface marker that distinguishes T cells with cytotoxic capacity. GPR56+CD4+T cells were significantly more prominent in the blood of patients with inactive as compared to active disease, suggesting that these cells were affected by immunosuppression and/or that they migrated from the circulation to sites of organ involvement. Indeed, GPR56+CD4+T cells were identified in T-cell infiltrates of affected kidneys and an association with immunosuppressive therapy was found. Moreover, distinct TCR gene segment usage and shared (public) T cell clones were found for the PR3-reactive TCRs. Shared T cell clones were found in different patients with AAV carrying the disease-associated HLA-DP allele, demonstrating convergence of the autoreactive T cell repertoire. Thus, we identified a CD4+T cell signature in blood and in affected kidneys that display PR3 autoreactivity and associates with T cell cytotoxicity. Our data provide a basis for novel rationales for both immune monitoring and future therapeutic intervention in PR3-AAV.

Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis.

OBJECTIVES: Advances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition. METHODS: Peripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment. RESULTS: The gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA. CONCLUSIONS: Our integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.

Biased TCR gene usage in citrullinated Tenascin C specific T-cells in rheumatoid arthritis.

We aimed to search for common features in the autoreactive T cell receptor (TCR) repertoire in patients with rheumatoid arthritis (RA), focusing on the newly identified candidate antigen citrullinated Tenascin C (cit-TNC). Mononuclear cells from peripheral blood or synovial fluid of eight RA-patients positive for the RA-associated HLA-DRB1*04:01 allele were in-vitro cultured with recently identified citrullinated peptides from Tenascin C. Antigen-specific T cells were isolated using peptide-HLA tetramer staining and subsequently single-cell sequenced for paired alpha/beta TCR analyses by bioinformatic tools. TCRs were re-expressed for further studies of antigen-specificity and T cell responses. Autoreactive T cell lines could be grown out from both peripheral blood and synovial fluid. We demonstrate the feasibility of retrieving true autoreactive TCR sequences by validating antigen-specificity in T cell lines with re-expressed TCRs. One of the Tenascin C peptides, cit-TNC22, gave the most robust T cell responses including biased TCR gene usage patterns. The shared TCR-beta chain signature among the cit-TNC22-specific TCRs was evident in blood and synovial fluid of different patients. The identification of common elements in the autoreactive TCR repertoire gives promise to the possibility of both immune monitoring of the autoimmune components in RA and of future antigen- or TCR-targeted specific intervention in subsets of patients.

Proposing the Promiscuous Protein Structures in JNK1 and JNK3 for Virtual Screening in Pursuit of Potential Leads.

Over the past decade, the available crystal structures have almost doubled in Protein Data Bank (PDB) providing the research community with a series of similar crystal structures to choose from for future docking studies. With the steady growth in the number of high-resolution three-dimensional protein structures, ligand docking-based virtual screening of chemical libraries to a receptor plays a critical role in the drug discovery process by identifying new drug candidates. Thus, identifying potential candidates among all the available structures in a database for docking studies is of utmost importance. Our work examined whether one could use the resolution of a number of known structures, without considering other parameters, to choose a good experimental structure for various docking studies to find more useful drug leads. We expected that a good experimental structure for docking studies to be the one that gave favorable docking with the largest number of ligands among the experimental structures to be selected. We chose three protein test systems for our study, all belonging to the family of MAPK: (1) JNK1, (2) JNK2, and (3) JNK3. On analysis of the results, the best resolution structures showed significant variations from the expected values in their result, whereas the poor resolution structures proved to be better candidates for docking studies.

Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.

BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. METHODS: We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. RESULTS: We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. CONCLUSIONS: This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

African relapsing Fever borreliae genomospecies revealed by comparative genomics.

BACKGROUND: Relapsing fever borreliae are vector-borne bacteria responsible for febrile infection in humans in North America, Africa, Asia, and in the Iberian Peninsula in Europe. Relapsing fever borreliae are phylogenetically closely related, yet they differ in pathogenicity and vectors. Their long-term taxonomy, based on geography and vector grouping, needs to be re-apprised in a genomic context. We therefore embarked into genomic analyses of relapsing fever borreliae, focusing on species found in Africa. RESULTS: Genome-wide phylogenetic analyses group Old World Borrelia crocidurae, Borrelia hispanica, B. duttonii, and B. recurrentis in one clade, and New World Borrelia turicatae and Borrelia hermsii in a second clade. Accordingly, average nucleotide identity is 99% among B. duttonii, B. recurrentis, and B. crocidurae and 96% between latter borreliae and B. hispanica while the similarity is 86% between Old World and New World borreliae. Comparative genomics indicates that the Old World relapsing fever B. duttonii, B. recurrentis, B. crocidurae, and B. hispanica have a 2,514-gene pan genome and a 933-gene core genome that includes 788 chromosomal and 145 plasmidic genes. Analyzing the role that natural selection has played in the evolution of Old World borreliae species revealed that 55 loci were under positive diversifying selection, including loci coding for membrane, flagellar, and chemotaxis proteins, three categories associated with adaption to specific niches. CONCLUSION: Genomic analyses led to a reappraisal of the taxonomy of relapsing fever borreliae in Africa. These analyses suggest that B. crocidurae, B. duttonii, and B. recurrentis are ecotypes of a unique genomospecies, while B. hispanica is a distinct species.

Draft genome sequences of Terra1 and Terra2 viruses, new members of the family Mimiviridae isolated from soil.

Since the discovery of Mimivirus, the founding member of the family Mimiviridae, three lineages, A-C, have been delineated among the mimiviruses of amoebae. To date, all giant viruses with annotated genomes have been isolated from water samples. Here, we describe the genome of two mimiviruses, Terra1 virus and Terra2 virus, which were recovered by co-culturing on Acanthamoeba spp. from soil samples. These genomes are predicted to harbor 1055 and 890 genes, respectively. Comparative genomics and phylogenomics show that Terra1 virus and Terra2 virus are classified within lineages C and A of the amoebae-associated mimiviruses, respectively. The genomic architecture of both viruses show conserved collinear central regions flanked by less conserved areas towards the extremities, when compared with other mimivirus genomes. A cluster of genes that are orthologous to bacterial genes and have no counterpart in other viral genomes except in lineage C mimiviruses was identified in Terra1 virus.

Complete genome sequence of Courdo11 virus, a member of the family Mimiviridae.

Giant viruses of amoebae were discovered 10 years ago and led to the description of two new viral families: Mimiviridae and Marseilleviridae. These viruses exhibit remarkable features, including large capsids and genomes that are similar in size to those of small bacteria and their large genetic repertoires include genes that are unique among viruses. The family Mimiviridae has grown during the past decade since the discovery of its initial member, Mimivirus, and continues to expand. Here, we describe the genome of a new giant virus that infects Acanthamoeba spp., Courdo11 virus, isolated in 2010 by inoculating Acanthamoeba spp. with freshwater collected from a river in southeastern France. The Courdo11 virus genome is a double stranded DNA molecule composed of 1,245,674 nucleotides. The comparative analyses of Courdo11 virus with the genomes of other giant viruses showed that it belongs to lineage C of mimiviruses of amoebae, being most closely related to Megavirus chilensis and LBA 111, the first mimivirus isolated from a human. Major characteristics of the M. chilensis genome were identified in the Courdo11 virus genome, found to encode three more tRNAs. Genomic architecture comparisons mirrored previous findings that showed conservation of collinear regions in the middle part of the genome and diversity towards the extremities. Finally, fourteen ORFans were identified in the Courdo11 virus genome, suggesting that the pan-genome of mimiviruses of amoeba might reach a plateau.

Evidence of the megavirome in humans.

BACKGROUND: Megavirales is a proposed new virus order composed of Mimivirus, Marseillevirus and closely related viruses, as well as members of the families Poxviridae, Iridoviridae, Ascoviridae, Phycodnaviridae and Asfarviridae. The Megavirales virome, which we refer to as the megavirome, has been largely neglected until now because of the use of technical procedures that have jeopardized the discovery of giant viruses, particularly the use of filters with pore sizes in the 0.2-0.45-µm range. Concurrently, there has been accumulating evidence supporting the role of Mimivirus, discovered while investigating a pneumonia outbreak using amoebal coculture, as a causative agent in pneumonia. OBJECTIVES: In this paper, we describe the detection of sequences related to Mimivirus and Marseillevirus in the gut microbiota from a young Senegalese man. We also searched for sequences related to Megavirales in human metagenomes publicly available in sequence databases. RESULTS: We serendipitously detected Mimivirus- and Marseillevirus-like sequences while using a new metagenomic approach targeting bacterial DNA that subsequently led to the isolation of a new member of the family Marseilleviridae, named Senegalvirus, from human stools. This discovery demonstrates the possibility of the presence of giant viruses of amoebae in humans. In addition, we detected sequences related to Megavirales members in several human metagenomes, which adds to previous findings by several groups. CONCLUSIONS: Overall, we present convergent evidence of the presence of mimiviruses and marseilleviruses in humans. Our findings suggest that we should re-evaluate the human megavirome and investigate the prevalence, diversity and potential pathogenicity of giant viruses in humans.

"Marseilleviridae", a new family of giant viruses infecting amoebae.

The family "Marseilleviridae" is a new proposed taxon for giant viruses that infect amoebae. Its first member, Acanthamoeba polyphaga marseillevirus (APMaV), was isolated in 2007 by culturing on amoebae a water sample collected from a cooling tower in Paris, France. APMaV has an icosahedral shape with a diameter of ≈250 nm. Its genome is a double-stranded circular DNA that is 368,454 base pairs (bp) in length. The genome has a GC content of 44.7 % and is predicted to encode 457 proteins. Phylogenetic reconstructions showed that APMaV belongs to a new viral family among nucleocytoplasmic large DNA viruses, a group of viruses that also includes Acanthamoeba polyphaga mimivirus (APMV) and the other members of the family Mimiviridae as well as the members of the families Poxviridae, Phycodnaviridae, Iridoviridae, Ascoviridae, and Asfarviridae. In 2011, Acanthamoeba castellanii lausannevirus (ACLaV), another close relative of APMaV, was isolated from river water in France. The ACLaV genome is 346,754 bp in size and encodes 450 genes, among which 320 have an APMaV protein as the closest homolog. Two other giant viruses closely related to APMaV and ACLaV have been recovered in our laboratory from a freshwater sample and a human stool sample using an amoebal co-culture method. The only currently identified hosts for "marseilleviruses" are Acanthamoeba spp. The prevalence of these viruses in the environment and in animals and humans remains to be determined.

Related giant viruses in distant locations and different habitats: Acanthamoeba polyphaga moumouvirus represents a third lineage of the Mimiviridae that is close to the megavirus lineage.

The 1,021,348 base pair genome sequence of the Acanthamoeba polyphaga moumouvirus, a new member of the Mimiviridae family infecting Acanthamoeba polyphaga, is reported. The moumouvirus represents a third lineage beside mimivirus and megavirus. Thereby, it is a new member of the recently proposed Megavirales order. This giant virus was isolated from a cooling tower water in southeastern France but is most closely related to Megavirus chiliensis, which was isolated from ocean water off the coast of Chile. The moumouvirus is predicted to encode 930 proteins, of which 879 have detectable homologs. Among these predicted proteins, for 702 the closest homolog was detected in Megavirus chiliensis, with the median amino acid sequence identity of 62%. The evolutionary affinity of moumouvirus and megavirus was further supported by phylogenetic tree analysis of conserved genes. The moumouvirus and megavirus genomes share near perfect orthologous gene collinearity in the central part of the genome, with the variations concentrated in the terminal regions. In addition, genomic comparisons of the Mimiviridae reveal substantial gene loss in the moumouvirus lineage. The majority of the remaining moumouvirus proteins are most similar to homologs from other Mimiviridae members, and for 27 genes the closest homolog was found in bacteria. Phylogenetic analysis of these genes supported gene acquisition from diverse bacteria after the separation of the moumouvirus and megavirus lineages. Comparative genome analysis of the three lineages of the Mimiviridae revealed significant mobility of Group I self-splicing introns, with the highest intron content observed in the moumouvirus genome.