Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass.

Kidney transplant (KTx) biopsies showing transplant glomerulopathy (TG) (glomerular basement membrane double contours (cg) > 0) and microvascular inflammation (MVI) in the absence of C4d staining and donor-specific antibodies (DSAs) do not fulfill the criteria for chronic active antibody-mediated rejection (CA-AMR) diagnosis and do not fit into any other Banff category. To investigate this, we initiated a multicenter intercontinental study encompassing 36 cases, comparing the immunomic and transcriptomic profiles of 14 KTx biopsies classified as cg+MVI DSA-/C4d- with 22 classified as CA-AMR DSA+/C4d+ through novel transcriptomic analysis using the NanoString Banff-Human Organ Transplant (B-HOT) panel and subsequent orthogonal subset analysis using two innovative 5-marker multiplex immunofluorescent panels. Nineteen genes were differentially expressed between the two study groups. Samples diagnosed with CA-AMR DSA+/C4d+ showed a higher glomerular abundance of natural killer cells and higher transcriptomic cell type scores for macrophages in an environment characterized by increased expression of complement-related genes (i.e., C5AR1) and higher activity of angiogenesis, interstitial fibrosis tubular atrophy, CA-AMR, and DSA-related pathways when compared to samples diagnosed with cg+MVI DSA-/C4d-. Samples diagnosed with cg+MVI DSA-/C4d- displayed a higher glomerular abundance and activity of T cells (CD3+, CD3+CD8+, and CD3+CD8-). Thus, we show that using novel multiomic techniques, KTx biopsies with cg+MVI DSA-/C4d- have a prominent T-cell presence and activity, putting forward the possibility that these represent a more T-cell dominant phenotype.

Prediction of molecular subclasses of uveal melanoma by deep learning using routine haematoxylin-eosin-stained tissue slides.

AIMS: Uveal melanoma has a high propensity to metastasize. Prognosis is associated with specific driver mutations and copy number variations, and these can only be obtained after genetic testing. In this study we evaluated the efficacy of patient outcome prediction using deep learning on haematoxylin and eosin (HE)-stained primary uveal melanoma slides in comparison to molecular testing. METHODS: In this retrospective study of patients with uveal melanoma, 113 patients from the Erasmus Medical Centre who underwent enucleation had tumour tissue analysed for molecular classification between 1993 and 2020. Routine HE-stained slides were scanned to obtain whole-slide images (WSI). After annotation of regions of interest, tiles of 1024 × 1024 pixels were extracted at a magnification of 40×. An ablation study to select the best-performing deep-learning model was carried out using three state-of-the-art deep-learning models (EfficientNet, Vision Transformer, and Swin Transformer). RESULTS: Deep-learning models were subjected to a training cohort (n = 40), followed by a validation cohort (n = 20), and finally underwent a test cohort (n = 48). A k-fold cross-validation (k = 3) of validation and test cohorts (n = 113 of three classes: BAP1, SF3B1, EIF1AX) demonstrated Swin Transformer as the best-performing deep-learning model to predict molecular subclasses based on HE stains. The model achieved an accuracy of 0.83 ± 0.09 on the validation cohort and 0.75 ± 0.04 on the test cohort. Within the subclasses, this model correctly predicted 70% BAP1-mutated, 61% SF3B1-mutated and 80% EIF1AX-mutated UM in the test set. CONCLUSIONS: This study showcases the potential of the deep-learning methodology for predicting molecular subclasses in a multiclass manner using HE-stained WSI. This development holds promise for advanced prognostication of UM patients without the need of molecular or immunohistochemical testing. Additionally, this study suggests there are distinct histopathological features per subclass; mainly utilizing epithelioid cellular morphology for BAP1-classification, but an unknown feature distinguishes EIF1AX and SF3B1.

Ex vivo drug sensitivity screening predicts response to temozolomide in glioblastoma patients and identifies candidate biomarkers.

BACKGROUND: Patient-derived glioma stem-like cells (GSCs) have become the gold-standard in neuro-oncological research; however, it remains to be established whether loss of in situ microenvironment affects the clinically-predictive value of this model. We implemented a GSC monolayer system to investigate in situ-in vitro molecular correspondence and the relationship between in vitro and patient response to temozolomide (TMZ). METHODS: DNA/RNA-sequencing was performed on 56 glioblastoma tissues and 19 derived GSC cultures. Sensitivity to TMZ was screened across 66 GSC cultures. Viability readouts were related to clinical parameters of corresponding patients and whole-transcriptome data. RESULTS: Tumour DNA and RNA sequences revealed strong similarity to corresponding GSCs despite loss of neuronal and immune interactions. In vitro TMZ screening yielded three response categories which significantly correlated with patient survival, therewith providing more specific prediction than the binary MGMT marker. Transcriptome analysis identified 121 genes related to TMZ sensitivity of which 21were validated in external datasets. CONCLUSION: GSCs retain patient-unique hallmark gene expressions despite loss of their natural environment. Drug screening using GSCs predicted patient response to TMZ more specifically than MGMT status, while transcriptome analysis identified potential biomarkers for this response. GSC drug screening therefore provides a tool to improve drug development and precision medicine for glioblastoma.

WeFaceNano: a user-friendly pipeline for complete ONT sequence assembly and detection of antibiotic resistance in multi-plasmid bacterial isolates.

BACKGROUND: Bacterial plasmids often carry antibiotic resistance genes and are a significant factor in the spread of antibiotic resistance. The ability to completely assemble plasmid sequences would facilitate the localization of antibiotic resistance genes, the identification of genes that promote plasmid transmission and the accurate tracking of plasmid mobility. However, the complete assembly of plasmid sequences using the currently most widely used sequencing platform (Illumina-based sequencing) is restricted due to the generation of short sequence lengths. The long-read Oxford Nanopore Technologies (ONT) sequencing platform overcomes this limitation. Still, the assembly of plasmid sequence data remains challenging due to software incompatibility with long-reads and the error rate generated using ONT sequencing. Bioinformatics pipelines have been developed for ONT-generated sequencing but require computational skills that frequently are beyond the abilities of scientific researchers. To overcome this challenge, the authors developed 'WeFaceNano', a user-friendly Web interFace for rapid assembly and analysis of plasmid DNA sequences generated using the ONT platform. WeFaceNano includes: a read statistics report; two assemblers (Miniasm and Flye); BLAST searching; the detection of antibiotic resistance- and replicon genes and several plasmid visualizations. A user-friendly interface displays the main features of WeFaceNano and gives access to the analysis tools. RESULTS: Publicly available ONT sequence data of 21 plasmids were used to validate WeFaceNano, with plasmid assemblages and anti-microbial resistance gene detection being concordant with the published results. Interestingly, the "Flye" assembler with "meta" settings generated the most complete plasmids. CONCLUSIONS: WeFaceNano is a user-friendly open-source software pipeline suitable for accurate plasmid assembly and the detection of anti-microbial resistance genes in (clinical) samples where multiple plasmids can be present.

Molecular characterization of colorectal adenomas reveals POFUT1 as a candidate driver of tumor progression.

Removal of colorectal adenomas is an effective strategy to reduce colorectal cancer (CRC) mortality rates. However, as only a minority of adenomas progress to cancer, such strategies may lead to overtreatment. The present study aimed to characterize adenomas by in-depth molecular profiling, to obtain insights into altered biology associated with the colorectal adenoma-to-carcinoma progression. We obtained low-coverage whole genome sequencing, RNA sequencing and tandem mass spectrometry data for 30 CRCs, 30 adenomas and 18 normal adjacent colon samples. These data were used for DNA copy number aberrations profiling, differential expression, gene set enrichment and gene-dosage effect analysis. Protein expression was independently validated by immunohistochemistry on tissue microarrays and in patient-derived colorectal adenoma organoids. Stroma percentage was determined by digital image analysis of tissue sections. Twenty-four out of 30 adenomas could be unambiguously classified as high risk (n = 9) or low risk (n = 15) of progressing to cancer, based on DNA copy number profiles. Biological processes more prevalent in high-risk than low-risk adenomas were related to proliferation, tumor microenvironment and Notch, Wnt, PI3K/AKT/mTOR and Hedgehog signaling, while metabolic processes and protein secretion were enriched in low-risk adenomas. DNA copy number driven gene-dosage effect in high-risk adenomas and cancers was observed for POFUT1, RPRD1B and EIF6. Increased POFUT1 expression in high-risk adenomas was validated in tissue samples and organoids. High POFUT1 expression was also associated with Notch signaling enrichment and with decreased goblet cells differentiation. In-depth molecular characterization of colorectal adenomas revealed POFUT1 and Notch signaling as potential drivers of tumor progression.

Transcriptome Analysis of Infected and Bystander Type 2 Alveolar Epithelial Cells during Influenza A Virus Infection Reveals In Vivo Wnt Pathway Downregulation.

Influenza virus outbreaks remain a serious threat to public health. A greater understanding of how cells targeted by the virus respond to the infection can provide insight into the pathogenesis of disease. Here we examined the transcriptional profile of in vivo-infected and uninfected type 2 alveolar epithelial cells (AEC) in the lungs of influenza virus-infected mice. We show for the first time the unique gene expression profiles induced by the in vivo infection of AEC as well as the transcriptional response of uninfected bystander cells. This work allows us to distinguish the direct and indirect effects of infection at the cellular level. Transcriptome analysis revealed that although directly infected and bystander AEC from infected animals shared many transcriptome changes compared to AEC from uninfected animals, directly infected cells produce more interferon and express lower levels of Wnt signaling-associated transcripts, while concurrently expressing more transcripts associated with cell death pathways, than bystander uninfected AEC. The Wnt signaling pathway was downregulated in both in vivo-infected AEC and in vitro-infected human lung epithelial A549 cells. Wnt signaling did not affect type I and III interferon production by infected A549 cells. Our results reveal unique transcriptional changes that occur within infected AEC and show that influenza virus downregulates Wnt signaling. In light of recent findings that Wnt signaling is essential for lung epithelial stem cells, our findings reveal a mechanism by which influenza virus may affect host lung repair.IMPORTANCE Influenza virus infection remains a major public health problem. Utilizing a recombinant green fluorescent protein-expressing influenza virus, we compared the in vivo transcriptomes of directly infected and uninfected bystander cells from infected mouse lungs and discovered many pathways uniquely regulated in each population. The Wnt signaling pathway was downregulated in directly infected cells and was shown to affect virus but not interferon production. Our study is the first to discern the in vivo transcriptome changes induced by direct viral infection compared to mere exposure to the lung inflammatory milieu and highlight the downregulation of Wnt signaling. This downregulation has important implications for understanding influenza virus pathogenesis, as Wnt signaling is critical for lung epithelial stem cells and lung epithelial cell differentiation. Our findings reveal a mechanism by which influenza virus may affect host lung repair and suggest interventions that prevent damage or accelerate recovery of the lung.

Consensus molecular subtype classification of colorectal adenomas.

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 'metabolic subtype', which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the 'MSI immune' subtype. Eight adenomas (13%) were classified as the 'canonical' CMS2. No adenomas were classified as the 'mesenchymal' CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Antigen Receptor Galaxy: A User-Friendly, Web-Based Tool for Analysis and Visualization of T and B Cell Receptor Repertoire Data.

Antigen Receptor Galaxy (ARGalaxy) is a Web-based tool for analyses and visualization of TCR and BCR sequencing data of 13 species. ARGalaxy consists of four parts: the demultiplex tool, the international ImMunoGeneTics information system (IMGT) concatenate tool, the immune repertoire pipeline, and the somatic hypermutation (SHM) and class switch recombination (CSR) pipeline. Together they allow the analysis of all different aspects of the immune repertoire. All pipelines can be run independently or combined, depending on the available data and the question of interest. The demultiplex tool allows data trimming and demultiplexing, whereas with the concatenate tool multiple IMGT/HighV-QUEST output files can be merged into a single file. The immune repertoire pipeline is an extended version of our previously published ImmunoGlobulin Galaxy (IGGalaxy) virtual machine that was developed to visualize V(D)J gene usage. It allows analysis of both BCR and TCR rearrangements, visualizes CDR3 characteristics (length and amino acid usage) and junction characteristics, and calculates the diversity of the immune repertoire. Finally, ARGalaxy includes the newly developed SHM and CSR pipeline to analyze SHM and/or CSR in BCR rearrangements. It analyzes the frequency and patterns of SHM, Ag selection (including BASELINe), clonality (Change-O), and CSR. The functionality of the ARGalaxy tool is illustrated in several clinical examples of patients with primary immunodeficiencies. In conclusion, ARGalaxy is a novel tool for the analysis of the complete immune repertoire, which is applicable to many patient groups with disturbances in the immune repertoire such as autoimmune diseases, allergy, and leukemia, but it can also be used to address basic research questions in repertoire formation and selection.

XLF deficiency results in reduced N-nucleotide addition during V(D)J recombination.

Repair of DNA double-strand breaks (DSBs) by the nonhomologous end-joining pathway (NHEJ) is important not only for repair of spontaneous breaks but also for breaks induced in developing lymphocytes during V(D)J (variable [V], diversity [D], and joining [J] genes) recombination of their antigen receptor loci to create a diverse repertoire. Mutations in the NHEJ factor XLF result in extreme sensitivity for ionizing radiation, microcephaly, and growth retardation comparable to mutations in LIG4 and XRCC4, which together form the NHEJ ligation complex. However, the effect on the immune system is variable (mild to severe immunodeficiency) and less prominent than that seen in deficiencies of NHEJ factors ARTEMIS and DNA-dependent protein kinase catalytic subunit, with defects in the hairpin opening step, which is crucial and unique for V(D)J recombination. Therefore, we aimed to study the role of XLF during V(D)J recombination. We obtained clinical data from 9 XLF-deficient patients and performed immune phenotyping and antigen receptor repertoire analysis of immunoglobulin (Ig) and T-cell receptor (TR) rearrangements, using next-generation sequencing in 6 patients. The results were compared with XRCC4 and LIG4 deficiency. Both Ig and TR rearrangements showed a significant decrease in the number of nontemplated (N) nucleotides inserted by terminal deoxynucleotidyl transferase, which resulted in a decrease of 2 to 3 amino acids in the CDR3. Such a reduction in the number of N-nucleotides has a great effect on the junctional diversity, and thereby on the total diversity of the Ig and TR repertoire. This shows that XLF has an important role during V(D)J recombination in creating diversity of the repertoire by stimulating N-nucleotide insertion.

Development and evaluation of a culture-free microbiota profiling platform (MYcrobiota) for clinical diagnostics.

Microbiota profiling has the potential to greatly impact on routine clinical diagnostics by detecting DNA derived from live, fastidious, and dead bacterial cells present within clinical samples. Such results could potentially be used to benefit patients by influencing antibiotic prescribing practices or to generate new classical-based diagnostic methods, e.g., culture or PCR. However, technical flaws in 16S rRNA gene next-generation sequencing (NGS) protocols, together with the requirement for access to bioinformatics, currently hinder the introduction of microbiota analysis into clinical diagnostics. Here, we report on the development and evaluation of an "end-to-end" microbiota profiling platform (MYcrobiota), which combines our previously validated micelle PCR/NGS (micPCR/NGS) methodology with an easy-to-use, dedicated bioinformatics pipeline. The newly designed bioinformatics pipeline processes micPCR/NGS data automatically and summarizes the results in interactive, but simple web reports. In order to explore the utility of MYcrobiota in clinical diagnostics, 47 clinical samples (40 "damaged skin" samples and 7 synovial fluids) were investigated using routine bacterial culture as comparator. MYcrobiota confirmed the presence of bacterial DNA in 37/37 culture-positive samples and detected bacterial taxa in 2/10 culture-negative samples. Moreover, 36/38 potentially relevant aerobic bacterial taxa and 3/3 mixtures of anaerobic bacteria were identified using culture and MYcrobiota, with the sensitivity and specificity being 95%. Interestingly, the majority of the 448 bacterial taxa identified using MYcrobiota were not identified using culture, which could potentially have an impact on clinical decision-making. Taken together, the development of MYcrobiota is a promising step towards the introduction of microbiota analysis into clinical diagnostic laboratories.

FuMa: reporting overlap in RNA-seq detected fusion genes.

UNLABELLED: A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/ CONTACT: y.hoogstrate@erasmusmc.nl or a.stubbs@erasmusmc.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antigen receptor sequencing of paired bone marrow samples shows homogeneous distribution of acute lymphoblastic leukemia subclones.

In B-cell precursor acute lymphoblastic leukemia, the initial leukemic cells share the same antigen receptor gene rearrangements. However, due to ongoing rearrangement processes, leukemic cells with different gene rearrangement patterns can develop, resulting in subclone formation. We studied leukemic subclones and their distribution in the bone marrow and peripheral blood at diagnosis. Antigen receptor gene rearrangements (IGH, IGK, TRG, TRD, TRB) were analyzed by next-generation sequencing in seven paired bone marrow samples and five paired bone marrow-peripheral blood samples. Background-thresholds were defined, which enabled identification of leukemic gene rearrangements down to very low levels. Paired bone marrow analysis showed oligoclonality in all 7 patients and up to 34 leukemic clones per patient. Additional analysis of evolutionary-related IGH gene rearrangements revealed up to 171 leukemic clones per patient. Interestingly, overall 86% of all leukemic gene rearrangements, including small subclones, were present in both bone marrow samples (range per patient: 72-100%). Paired bone marrow-peripheral blood analysis showed that 83% of all leukemic gene rearrangements in bone marrow were also found in peripheral blood (range per patient: 81-100%). Remarkably, in the paired bone marrow samples and paired bone marrow-peripheral blood samples the vast majority of leukemic gene rearrangements had a similar frequency (<5-fold frequency difference) (96% and 96% of leukemic rearrangements, respectively). Together, these results indicate that B-cell precursor acute lymphoblastic leukemia is generally highly oligoclonal. Nevertheless, the vast majority of leukemic clones, even the minor antigen receptor-defined subclones, are homogeneously distributed throughout the bone marrow and peripheral blood compartment.

Identification of Associated Genes and Diseases in Patients With Congenital Upper-Limb Anomalies: A Novel Application of the OMT Classification.

PURPOSE: Congenital upper-limb anomalies (CULA) can present as a part of a syndrome or association. There is a wide spectrum of CULA, each of which might be related to different diseases. The structure provided by the Oberg, Manske, and Tonkin (OMT) classification could aid in differential diagnosis formulation in patients with CULA. The aims of this study were to review the Human Phenotype Ontology (HPO) project database for diseases and causative genes related to the CULA described in the OMT classification and to develop a methodology for differential diagnosis formulation based on the observed congenital anomalies, CulaPhen. METHODS: We reviewed the HPO database for all diseases, including causative genes related to CULA. All CULA were classified according to the OMT classification; associated non-hand phenotypes were classified into 12 anatomical groups. We analyzed the contribution of each anatomical group to a given disease and developed a tool for differential diagnosis formulation based on these contributions. We compared our results with cases from the literature and with a current HPO tool, Phenomizer. RESULTS: In total, 514 hand phenotypes were obtained, 384 of which could be classified in the OMT classification. A total of 1,403 diseases could be related to those CULA. A comparison with 10 recently published cases with CULA revealed that the presented phenotype matched the descriptions in our dataset. The differential diagnosis produced using our methodology was more accurate than Phenomizer in 4 of 5 examples. CONCLUSIONS: The OMT classification can be used to describe hand anomalies that may present in over 1,400 diseases. CulaPhen was developed to provide a (hand) phenotype-based differential diagnosis. Differential diagnosis formulation based on the proposed system outperforms the system in current use. CLINICAL RELEVANCE: This study illustrates that the OMT diagnoses, either individually or combined, can be cross-referenced with different diseases and syndromes. Therefore, use of the OMT classification can aid differential diagnosis formulation for CULA patients.

IL-7 Receptor Mutations and Steroid Resistance in Pediatric T cell Acute Lymphoblastic Leukemia: A Genome Sequencing Study.

BACKGROUND: Pediatric acute lymphoblastic leukemia (ALL) is the most common childhood cancer and the leading cause of cancer-related mortality in children. T cell ALL (T-ALL) represents about 15% of pediatric ALL cases and is considered a high-risk disease. T-ALL is often associated with resistance to treatment, including steroids, which are currently the cornerstone for treating ALL; moreover, initial steroid response strongly predicts survival and cure. However, the cellular mechanisms underlying steroid resistance in T-ALL patients are poorly understood. In this study, we combined various genomic datasets in order to identify candidate genetic mechanisms underlying steroid resistance in children undergoing T-ALL treatment. METHODS AND FINDINGS: We performed whole genome sequencing on paired pre-treatment (diagnostic) and post-treatment (remission) samples from 13 patients, and targeted exome sequencing of pre-treatment samples from 69 additional T-ALL patients. We then integrated mutation data with copy number data for 151 mutated genes, and this integrated dataset was tested for associations of mutations with clinical outcomes and in vitro drug response. Our analysis revealed that mutations in JAK1 and KRAS, two genes encoding components of the interleukin 7 receptor (IL7R) signaling pathway, were associated with steroid resistance and poor outcome. We then sequenced JAK1, KRAS, and other genes in this pathway, including IL7R, JAK3, NF1, NRAS, and AKT, in these 69 T-ALL patients and a further 77 T-ALL patients. We identified mutations in 32% (47/146) of patients, the majority of whom had a specific T-ALL subtype (early thymic progenitor ALL or TLX). Based on the outcomes of these patients and their prednisolone responsiveness measured in vitro, we then confirmed that these mutations were associated with both steroid resistance and poor outcome. To explore how these mutations in IL7R signaling pathway genes cause steroid resistance and subsequent poor outcome, we expressed wild-type and mutant IL7R signaling molecules in two steroid-sensitive T-ALL cell lines (SUPT1 and P12 Ichikawa cells) using inducible lentiviral expression constructs. We found that expressing mutant IL7R, JAK1, or NRAS, or wild-type NRAS or AKT, specifically induced steroid resistance without affecting sensitivity to vincristine or L-asparaginase. In contrast, wild-type IL7R, JAK1, and JAK3, as well as mutant JAK3 and mutant AKT, had no effect. We then performed a functional study to examine the mechanisms underlying steroid resistance and found that, rather than changing the steroid receptor's ability to activate downstream targets, steroid resistance was associated with strong activation of MEK-ERK and AKT, downstream components of the IL7R signaling pathway, thereby inducing a robust antiapoptotic response by upregulating MCL1 and BCLXL expression. Both the MEK-ERK and AKT pathways also inactivate BIM, an essential molecule for steroid-induced cell death, and inhibit GSK3B, an important regulator of proapoptotic BIM. Importantly, treating our cell lines with IL7R signaling inhibitors restored steroid sensitivity. To address clinical relevance, we treated primary T-ALL cells obtained from 11 patients with steroids either alone or in combination with IL7R signaling inhibitors; we found that including a MEK, AKT, mTOR, or dual PI3K/mTOR inhibitor strongly increased steroid-induced cell death. Therefore, combining these inhibitors with steroid treatment may enhance steroid sensitivity in patients with ALL. The main limitation of our study was the modest cohort size, owing to the very low incidence of T-ALL. CONCLUSIONS: Using an unbiased sequencing approach, we found that specific mutations in IL7R signaling molecules underlie steroid resistance in T-ALL. Future prospective clinical studies should test the ability of inhibitors of MEK, AKT, mTOR, or PI3K/mTOR to restore or enhance steroid sensitivity and improve clinical outcome.

Whole genome sequencing of a dizygotic twin suggests a role for the serotonin receptor HTR7 in autism spectrum disorder.

Whole genome sequencing of a severely affected dizygotic twin with an autism spectrum disorder and intellectual disability revealed a compound heterozygous mutation in the HTR7 gene as the only variation not detected in control databases. Each parent carries one allele of the mutation, which is not present in an unaffected stepsister. The HTR7 gene encodes the 5-HT7 serotonin receptor that is involved in brain development, synaptic transmission, and plasticity. The paternally inherited p.W60C variant is situated at an evolutionary conserved nucleotide and predicted damaging by Polyphen2. A mutation akin to the maternally inherited pV286I mutation has been reported to significantly affect the binding characteristics of the receptor. Therefore, the observed sequence alterations provide a first suggestive link between a genetic abnormality in the HTR7 gene and a neurodevelopmental disorder. © 2016 Wiley Periodicals, Inc.

Similar recombination-activating gene (RAG) mutations result in similar immunobiological effects but in different clinical phenotypes.

BACKGROUND: V(D)J recombination takes place during lymphocyte development to generate a large repertoire of T- and B-cell receptors. Mutations in recombination-activating gene 1 (RAG1) and RAG2 result in loss or reduction of V(D)J recombination. It is known that different mutations in RAG genes vary in residual recombinase activity and give rise to a broad spectrum of clinical phenotypes. OBJECTIVE: We sought to study the immunologic mechanisms causing the clinical spectrum of RAG deficiency. METHODS: We included 22 patients with similar RAG1 mutations (c.519delT or c.368_369delAA) resulting in N-terminal truncated RAG1 protein with residual recombination activity but presenting with different clinical phenotypes. We studied precursor B-cell development, immunoglobulin and T-cell receptor repertoire formation, receptor editing, and B- and T-cell numbers. RESULTS: Clinically, patients were divided into 3 main categories: T(-)B(-) severe combined immunodeficiency, Omenn syndrome, and combined immunodeficiency. All patients showed a block in the precursor B-cell development, low B- and T-cell numbers, normal immunoglobulin gene use, limited B- and T-cell repertoires, and slightly impaired receptor editing. CONCLUSION: This study demonstrates that similar RAG mutations can result in similar immunobiological effects but different clinical phenotypes, indicating that the level of residual recombinase activity is not the only determinant for clinical outcome. We postulate a model in which the type and moment of antigenic pressure affect the clinical phenotypes of these patients.

ImmunoGlobulin galaxy (IGGalaxy) for simple determination and quantitation of immunoglobulin heavy chain rearrangements from NGS.

BACKGROUND: Sequence analysis of immunoglobulin heavy chain (IGH) gene rearrangements and frequency analysis is a powerful tool for studying the immune repertoire, immune responses and immune dysregulation in health and disease. The challenge is to provide user friendly, secure and reproducible analytical services that are available for both small and large laboratories which are determining VDJ repertoire using NGS technology. RESULTS: In this study we describe ImmunoGlobulin Galaxy (IGGalaxy)- a convenient web based application for analyzing next-generation sequencing results and reporting IGH gene rearrangements for both repertoire and clonality studies. IGGalaxy has two analysis options one using the built in igBLAST algorithm and the second using output from IMGT; in either case repertoire summaries for the B-cell populations tested are available. IGGalaxy supports multi-sample and multi-replicate input analysis for both igBLAST and IMGT/HIGHV-QUEST. We demonstrate the technical validity of this platform using a standard dataset, S22, used for benchmarking the performance of antibody alignment utilities with a 99.9 % concordance with previous results. Re-analysis of NGS data from our samples of RAG-deficient patients demonstrated the validity and user friendliness of this tool. CONCLUSIONS: IGGalaxy provides clinical researchers with detailed insight into the repertoire of the B-cell population per individual sequenced and between control and pathogenic genomes. IGGalaxy was developed for 454 NGS results but is capable of analyzing alternative NGS data (e.g. Illumina, Ion Torrent). We demonstrate the use of a Galaxy virtual machine to determine the VDJ repertoire for reference data and from B-cells taken from immune deficient patients. IGGalaxy is available as a VM for download and use on a desktop PC or on a server.

NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data.

SUMMARY: The discovery of functionally related groups in a set of significantly abundant proteins from a mass spectrometry experiment is an important step in a proteomics analysis pipeline. Here we describe NetWeAvers (Network Weighted Averages) for analyzing groups of regulated proteins in a network context, e.g. as defined by clusters of protein-protein interactions. NetWeAvers is an R package that provides a novel method for analyzing proteomics data integrated with biological networks. The method includes an algorithm for finding dense clusters of proteins and a permutation algorithm to calculate cluster P-values. Optional steps include summarizing quantified peptide values to single protein values and testing for differential expression, such that the data input can simply be a list of identified and quantified peaks. AVAILABILITY AND IMPLEMENTATION: The NetWeAvers package is written in R, is open source and is freely available on CRAN and from netweavers.erasmusmc.nl under the GPL-v2 license. CONTACT: e.mcclellan@erasmusmc.nl

FAIR data retrieval for sensitive clinical research data in Galaxy.

BACKGROUND: In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized "omics" platform for FAIR data analysis. RESULTS: To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow. CONCLUSIONS: We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation.

Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.