Aims/hypothesis
The sodium–glucose cotransporter 2 (SGLT2) inhibitor canagliflozin slows progression of kidney function decline in type 2 diabetes. The aim of this study was to assess the effect of ...the SGLT2 inhibitor canagliflozin on biomarkers for progression of diabetic kidney disease (DKD).
Methods
A canagliflozin mechanism of action (MoA) network model was constructed based on an in vitro transcriptomics experiment in human proximal tubular cells and molecular features linked to SGLT2 inhibitors from scientific literature. This model was mapped onto an established DKD network model that describes molecular processes associated with DKD. Overlapping areas in both networks were subsequently used to select candidate biomarkers that change with canagliflozin therapy. These biomarkers were measured in 296 stored plasma samples from a previously reported 2 year clinical trial comparing canagliflozin with glimepiride.
Results
Forty-four proteins present in the canagliflozin MoA molecular model overlapped with proteins in the DKD network model. These proteins were considered candidates for monitoring impact of canagliflozin on DKD pathophysiology. For ten of these proteins, scientific evidence was available suggesting that they are involved in DKD progression. Of these, compared with glimepiride, canagliflozin 300 mg/day decreased plasma levels of TNF receptor 1 (TNFR1; 9.2%;
p
< 0.001), IL-6 (26.6%;
p
= 0.010), matrix metalloproteinase 7 (MMP7; 24.9%;
p
= 0.011) and fibronectin 1 (FN1; 14.9%;
p
= 0.055) during 2 years of follow-up.
Conclusions/interpretation
The observed reduction in TNFR1, IL-6, MMP7 and FN1 suggests that canagliflozin contributes to reversing molecular processes related to inflammation, extracellular matrix turnover and fibrosis.
Trial registration ClinicalTrials.gov NCT00968812
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
encodes a TGF-
superfamily member that is rapidly activated in response to stress in multiple organ systems, including the kidney. However, there has been a lack of information about
activity and ...effects in normal kidney and in AKI.
We used genome editing to generate a
mouse line, removing
at the targeted allele, and enabling direct visualization and genetic modification of
-expressing cells. We extensively mapped
expression in the normal kidney and following bilateral ischemia-reperfusion injury, and quantified and compared renal responses to ischemia-reperfusion injury in the presence and absence of GDF15. In addition, we analyzed single nucleotide polymorphism association data for GDF15 for associations with patient kidney transplant outcomes.
is normally expressed within aquaporin 1-positive cells of the S3 segment of the proximal tubule, aquaporin 1-negative cells of the thin descending limb of the loop of Henle, and principal cells of the collecting system.
is rapidly upregulated within a few hours of bilateral ischemia-reperfusion injury at these sites and new sites of proximal tubule injury. Deficiency of
exacerbated acute tubular injury and enhanced inflammatory responses. Analysis of clinical transplantation data linked low circulating levels of GDF15 to an increased incidence of biopsy-proven acute rejection.
contributes to an early acting, renoprotective injury response, modifying immune cell actions. The data support further investigation in clinical model systems of the potential benefit from GDF15 administration in situations in which some level of tubular injury is inevitable, such as following a kidney transplant.
Productivity in drug R&D continues seeing significant attrition in clinical stage testing. Approval of new molecular entities proceeds with slow pace specifically when it comes to chronic, ...age-related diseases, calling for new conceptual approaches, methodological implementation and organizational adoption in drug development.
Detailed phenotyping of disease presentation together with comprehensive representation of drug mechanism of action is considered as a path forward, and a big data spectrum has become available covering behavioral, clinical and molecular characteristics, the latter combining reductionist and explorative strategies. On this basis integrative analytics in the realm of Systems Biology has emerged, essentially aiming at traversing associations into causal relationships for bridging molecular disease specifics and clinical phenotype surrogates and finally explaining drug response and outcome.
From a conceptual perspective bottom-up modeling approaches are available, with dynamical hierarchies as formalism capable of describing clinical findings as emergent properties of an underlying molecular process network comprehensively resembling disease pathology. In such representation biomarker candidates serve as proxy of a molecular process set, at the interface of a corresponding representation of drug mechanism of action allowing patient stratification and prediction of drug response. In practical implementation network analytics on a protein coding gene level has provided a number of example cases for matching disease presentation and drug molecular effect, and workflows combining computational hypothesis generation and experimental evaluation have become available for systematically optimizing biomarker candidate selection.
With biomarker-based enrichment strategies in adaptive clinical trials, implementation routes for tackling development attrition are provided. Predictive biomarkers add precision in drug development and as companion diagnostics in clinical practice.
Synthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions ...results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy.
We established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.
Twelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies.
A set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Microbial consortia execute collaborative molecular processes with contributions from individual species, on such basis enabling optimized molecular function. Such collaboration and synergies benefit ...metabolic flux specifically in extreme environmental conditions as seen in acid mine drainage, with biofilms as relevant microenvironment. However, knowledge about community species composition is not sufficient for deducing presence and efficiency of composite molecular function. For this task molecular resolution of the consortium interactome is to be retrieved, with molecular biomarkers particularly suited for characterizing composite molecular processes involved in biofilm formation and maintenance. A microbial species set identified in 18 copper environmental sites provides a data matrix for deriving a cross-species molecular process model of biofilm formation composed of 191 protein coding genes contributed from 25 microbial species. Computing degree and stress centrality of biofilm molecular process nodes allows selection of network hubs and central connectors, with the top ranking molecular features proposed as biomarker candidates for characterizing biofilm homeostasis. Functional classes represented in the biomarker panel include quorum sensing, chemotaxis, motility and extracellular polysaccharide biosynthesis, complemented by chaperones. Abundance of biomarker candidates identified in experimental data sets monitoring different biofilm conditions provides evidence for the selected biomarkers as sensitive and specific molecular process proxies for capturing biofilm microenvironments. Topological criteria of process networks covering an aggregate function of interest support the selection of biomarker candidates independent of specific community species composition. Such panels promise efficient screening of environmental samples for presence of microbial community composite molecular function.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons.
We generated molecular models representing ...pathophysiological mechanisms of DN and tacrolimus mechanism of action (MoA) based on literature derived data and transcriptomics datasets. Shared enriched molecular pathways were identified based on both model datasets. A newly generated transcriptomics dataset studying the effect of tacrolimus on mesangial cells in vitro was added to identify mechanisms in DN pathophysiology. We searched for features in interference between the DN molecular model and the tacrolimus MoA molecular model already holding annotation evidence as diagnostic or prognostic biomarker in the context of DN.
Thirty nine molecular features were shared between the DN molecular model, holding 252 molecular features and the tacrolimus MoA molecular model, holding 209 molecular features, with six additional molecular features affected by tacrolimus in mesangial cells. Significantly affected molecular pathways by both molecular model sets included cytokine-cytokine receptor interactions, adherens junctions, TGF-beta signaling, MAPK signaling, and calcium signaling. Molecular features involved in inflammation and immune response contributing to DN progression were significantly downregulated by tacrolimus (e.g. the tumor necrosis factor alpha (TNF), interleukin 4, or interleukin 10). On the other hand, pro-fibrotic stimuli being detrimental to renal function were induced by tacrolimus like the transforming growth factor beta 1 (TGFB1), endothelin 1 (EDN1), or type IV collagen alpha 1 (COL4A1).
Patients with DN and elevated TNF levels might benefit from tacrolimus treatment regarding maintaining GFR and reducing inflammation. TGFB1 and EDN1 are proposed as monitoring markers to assess degree of renal damage. Next to this stratification approach, the use of drug combinations consisting of tacrolimus in addition to ACE inhibitors, angiotensin receptor blockers, TGFB1- or EDN1-receptor antagonists might warrant further studies.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Acute kidney injury (AKI) affects roughly 25% of all recipients of deceased donor organs. The prevention of post-transplant AKI is still an unmet clinical need. We prospectively collected zero-hour, ...indication as well as protocol kidney biopsies from 166 allografts between 2011 and 2013. In this cohort eight cases with AKI and ten matched allografts without pathology serving as control group were identified with a follow-up biopsy within the first twelve days after engraftment. For this set the zero-hour and follow-up biopsies were subjected to genome wide microRNA and mRNA profiling and analysis, followed by validation in independent expression profiles of 42 AKI and 21 protocol biopsies for strictly controlling the false discovery rate. Follow-up biopsies of AKI allografts compared to time-matched protocol biopsies, further baseline adjustment for zero-hour biopsy expression level and validation in independent datasets, revealed a molecular AKI signature holding 20 mRNAs and two miRNAs (miR-182-5p and miR-21-3p). Next to several established biomarkers such as lipocalin-2 also novel candidates of interest were identified in the signature. In further experimental evaluation the elevated transcript expression level of the secretory leukocyte peptidase inhibitor (SLPI) in AKI allografts was confirmed in plasma and urine on the protein level (p<0.001 and p = 0.003, respectively). miR-182-5p was identified as a molecular regulator of post-transplant AKI, strongly correlated with global gene expression changes during AKI. In summary, we identified an AKI-specific molecular signature providing the ground for novel biomarkers and target candidates such as SLPI and miR-182-5p in addressing AKI.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Introduction
Antibody mediated rejection (ABMR) is a major factor limiting outcome after organ transplantation. Anti-HLA donor-specific antibodies (DSA) of the IgG isotype are mainly responsible for ...ABMR. Recently DSA of the IgE isotype were demonstrated in murine models as well as in a small cohort of sensitized transplant recipients. In the present study, we aimed to determine the frequency of pre-existing and
de novo
anti-HLA IgE antibodies in a cohort of 105 solid organ transplant recipients.
Methods
We prospectively measured anti-HLA IgE antibodies in a cohort of kidney (n=60), liver, heart and lung (n=15 each) transplant recipients before and within one-year after transplantation, employing a single-antigen bead assay for HLA class I and class II antigens. Functional activity of anti-HLA IgE antibodies was assessed by an
in vitro
mediator release assay. Antibodies of the IgG1-4 subclasses and Th1 and Th2 cytokines were measured in anti-HLA IgE positive patients.
Results
Pre-existing anti-HLA IgE antibodies were detected in 10% of renal recipients (including 3.3% IgE-DSA) and in 4.4% of non-renal solid organ transplant recipients (heart, liver and lung cohort). Anti-HLA IgE occurred only in patients that were positive for anti-HLA IgG, and most IgE positive patients had had a previous transplant. Only a small fraction of patients developed
de novo
anti-HLA IgE antibodies (1.7% of kidney recipients and 4.4% of non-renal recipients), whereas no
de novo
IgE-DSA was detected. IgG subclass antibodies showed a distinct pattern in patients who were positive for anti-HLA IgE. Moreover, patients with anti-HLA IgE showed elevated Th2 and also Th1 cytokine levels. Serum from IgE positive recipients led to degranulation of basophils
in vitro
, demonstrating functionality of anti-HLA IgE.
Discussion
These data demonstrate that anti-HLA IgE antibodies occur at low frequency in kidney, liver, heart and lung transplant recipients. Anti-HLA IgE development is associated with sensitization at the IgG level, in particular through previous transplants and distinct IgG subclasses. Taken together, HLA specific IgE sensitization is a new phenomenon in solid organ transplant recipients whose potential relevance for allograft injury requires further investigation.
Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field ...of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior.
ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples.
IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/ .
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Pre-sensitized kidney transplant recipients have a higher risk for rejection following kidney transplantation and therefore receive lymphodepletional induction therapy with anti-human T-lymphocyte ...globulin (ATLG) whereas non-sensitized patients are induced in many centers with basiliximab. The time course of lymphocyte reconstitution with regard to the overall and donor-reactive T-cell receptor (TCR) specificity remains elusive.
Five kidney transplant recipients receiving a 1.5-mg/kg ATLG induction therapy over 7 days and five patients with 2 × 20 mg basiliximab induction therapy were longitudinally monitored. Peripheral mononuclear cells were sampled pre-transplant and within 1, 3, and 12 months after transplantation, and their overall and donor-reactive TCRs were determined by next-generation sequencing of the TCR beta CDR3 region. Overall TCR repertoire diversity, turnover, and donor specificity were assessed at all timepoints.
We observed an increase in the donor-reactive TCR repertoire after transplantation in patients, independent of lymphocyte counts or induction therapy. Donor-reactive CD4 T-cell frequency in the ATLG group increased from 1.14% + -0.63 to 2.03% + -1.09 and from 0.93% + -0.63 to 1.82% + -1.17 in the basiliximab group in the first month. Diversity measurements of the entire T-cell repertoire and repertoire turnover showed no statistical difference between the two induction therapies. The difference in mean clonality between groups was 0.03 and 0.07 pre-transplant in the CD4 and CD8 fractions, respectively, and was not different over time (CD4: F(1.45, 11.6) = 0.64 p = 0.496; CD8: F(3, 24) = 0.60 p = 0.620). The mean difference in R20, a metric for immune dominance, between groups was -0.006 in CD4 and 0.001 in CD8 T-cells and not statistically different between the groups and subsequent timepoints (CD4: F(3, 24) = 0.85 p = 0.479; CD8: F(1.19, 9.52) = 0.79 p = 0.418).
Reduced-dose ATLG induction therapy led to an initial lymphodepletion followed by an increase in the percentage of donor-reactive T-cells after transplantation similar to basiliximab induction therapy. Furthermore, reduced-dose ATLG did not change the overall TCR repertoire in terms of a narrowed or skewed TCR repertoire after immune reconstitution, comparable to non-depletional induction therapy.
PDF
