br Experimental design materials and methods br Acknowledgem

Experimental design, materials and methods
Acknowledgements G.A. is the recipient of a Senior Clinical Research Scholar of the Fonds de Recherche du Québec – Santé. Funding for this study provided by the Canadian Institutes of Health Research (PJT-148718) to G.A. and S.N. and the Banque Nationale Research Chair in Cardiovascular Genetics to G.A. Additional funding was provided by the Canadian Institutes of Health Research and the Fonds de Recherche du Québec (34574) through the E-RARE initiative CoHEART. We thank Dr. Alyson Fournier׳s lab for the TUBB3 and RBPMS antibodies.
Data The presented dataset contains the bacterial composition of free cyanide (CDO) and thiocyanate degrading (TDO) organisms from electroplating and synthetic SCN- containing wastewater, respectively. Table 1 shows the comparative analysis of the bacterial compositions between the CDOs and TDOs.
Experimental design, materials and methods
Acknowledgements
Data The data shown in this manuscript have been generated in a study of FCRL4+ and FcRL4- B cells infiltrating the synovial fluid and synovial tissue of RA patients. They include a link to the GEO dataset of RNAseq gene expression profiles of these cells. Furthermore, the Ig isotype distribution of the B cells for these populations is shown for four individual patients in Fig. 1. Table 1 gives detailed clinical characteristics from the anonymized patients. These are linked to the data shown in Table 2, detailing variable gene region sequences from sorted cells, the isotype usage and reactivity with citrullinated proteins of these individual cells. Table 3 displays the number of sequences and recombinant monoclonal buy GDC-0152 generated from FcRL4+ and FcRL4- B cells from individual patients.
Experimental design, materials and methods More detailed information can be found in Ref. [1].
Acknowledgements We are grateful for excellent technical assistance from Peter Sahlström (KI) and Holly Adams (Birmingham). This study was supported by grants from the Karolinska Institutet Foundations for Rheumatology Research, (2014reum42676) the Nanna Svartz foundation (2015-00077), the Knut and Alice Wallenberg foundation, the Swedish research council (VR)​ (2012-02677 and 2015-02900), the Swedish association against rheumatism, the Börje Dahlin Foundation, the King Gustaf V 80-year Foundation, the Ulla and Gustaf af Ugglas Foundation (2014uggl42674), and the VR-supported Linneaus consortium (CERIC) (2007-8703) to VM and KA. Furthermore, this work was supported by MRC DPFS grant code MR/M007669/1 to DS-T as well as a Wellcome Trust ISSF (097825/Z/11/A) grant. We received funding from European Community׳s Collaborative projectFP7-HEALTH-F2-2012-305549 “EURO TEAM”. AF was supported by an Arthritis Research UK Clinician Scientist Award18547. The Arthritis Research UK Rheumatoid Arthritis Pathogenesis Centre of Excellence – RACE, - is part-funded by Arthritis Research UK through grant number 20298.
Data The data of this article summarize the identity and accession numbers of sequencing data files (Table 1), the sizes of the sequence sets during the different stages of data processing (Table 2), and the outcome of validation of new inferred genes/alleles (Table 3), identified by use of IgDiscover and TIgGER. The frequencies of readily inferable [2] IGHD (Immunoglobulin heavy D-gene) genes used by the two haplotypes of five subjects are summarized (Table 4). Furthermore the data illustrate the effect of using a germline gene database that extends beyond codon 105 on gene inference (Fig. 1), and summarizes the outcome of TIgGER-based germline gene inference of six transcriptoms (Fig. 2). The data also illustrates how low sequencing quality scores are associated with some, but certainly not all, inferred germline gene alleles (Fig. 3), and summarizes IGHJ (Immunoglobulin heavy J-gene) alleles used by transcriptomes of six subjects (Fig. 4). The link between inferred IGHV (Immunoglobulin heavy V-gene) germline genes/alleles and different alleles of IGHJ6 in bone marrow (BM)- and peripheral blood (PB)-derived transcriptomes of two heterozygous subjects is shown (Fig. 5). The data summarizes linkage of different IGHD genes to two different haplotypes defined by alleles of IGHJ6 or defined by heterozygous IGHV genes (Fig. 6). The linkage of IGHV1-8, IGHV3-9, IGHV5-10-1, and IGHV3-64D germline genes to different haplotypes in subjects with two different IGHD gene-defined haplotypes (Fig. 7) is shown. Association of IGHV germline genes/alleles with particular IGHD genes in five subjects with different IGHD-defined haplotypes is shown (Fig. 8), as is the extent of association of alleles of IGHV4-59 to particular IGHD genes (Fig. 9). Finally, data describing assessment of alleles of IGHD genes detected in IgM-encoding transcriptomes of six subjects (Fig. 10), and of IGHV germline genes associated to the different alleles of IGHD genes in two subjects (Fig. 11) is shown.