Diabetic kidney disease (DKD) is the leading cause of end stage renal disease worldwide and occurs in up to 40% of patients with diabetes. The standard of care for DKD treatment has not kept up with the current health epidemic, which has led to a heavy economic toll and substantial health burden. Targeting either cell surface (cs)GRP78, activated α2-macroglobulin (α2M*) or preventing their interaction may provide a novel anti-fibrotic therapeutic target for the treatment of DKD and potentially non-diabetic chronic kidney disease (CKD) as well. Previously our lab has shown that HG-induced csGRP78 is a mediator of PI3k/Akt signaling and downstream extracellular matrix (ECM) protein production in glomerular mesangial cells (MC). However, the ligand responsible for activating high glucose (HG)-induced csGRP78 had not yet been determined. We have shown thus far that α2M is endogenously produced, secreted, and activated (denoted α2M*) in HG by MC, which leads to its binding to and activation thereof csGRP78. Further, α2M knockdown or α2M* neutralization attenuated Akt activation, the production of the profibrotic cytokine connective growth tissue factor (CTGF) and ECM proteins fibronectin and collagen IV. We have also shown that integrin β1 (Intβ1), a transmembrane receptor, associated with csGRP78 under HG conditions and likely acts as a tether to present csGRP78 completely extracellularly on MC. Interestingly, Intβ1 activation, even in the absence of HG, was sufficient to induce csGRP78 translocation. Further, inhibition of either csGRP78 or Intβ1 prevented synthesis, secretion and signaling of TGFβ1. This data implicates a role for Intβ1 as a required signaling partner for csGRP78-mediated profibrotic signaling. To further our understanding of csGRP78/ α2M*’s role in DKD, we investigated their ability to mediate TGFβ1 signaling through its non-proteolytic activator thrombospondin-1 (TSP1). Here, HG-induced TSP1 expression, ECM deposition, and activation of TGFβ1 was regulated by the PI3k/Akt pathway via csGRP78/α2M* in MC. Furthermore, we assessed whether this csGRP78/ α2M* axis is relevant to promoting profibrotic signaling in other renal cell types, including proximal tubule epithelial cells (PTEC) and renal fibroblasts (RF), that contribute to the pathogenesis of both later stage DKD and non-diabetic CKD. We show evidence here that HG and direct treatment with TGFβ1, a key pathologic regulator of kidney fibrosis, induce GRP78 surface translocation as well as the endogenous production and activation of α2M in both PTEC and RF. Inhibition of either csGRP78 or α2M* prevented TGFβ1 signaling measured as Smad3 activation as well as downstream ECM production. Interestingly, inhibition of this pathway under direct TGFβ1 treatment did not prevent Smad3 activation, implicating a role for Smad-independent TGFβ1 signaling through this axis. We identified the known noncanonical TGFβ1 signaling partners, yes associated protein (YAP) and transcriptional co-activator with PDZ binding motif (TAZ), are mediated by csGRP78 and α2M*. Lastly, we evaluated the potential therapeutic benefit of inhibiting csGRP78/α2M* interaction in the kidney fibrosis model, unilateral ureteral obstruction (UUO). Here, we show evidence that inhibition of this signaling axis using an inhibitory peptide can prevent renal fibrosis. Whether this peptide also prevents fibrosis in DKD is currently being assessed. Together, these studies strongly implicate targeting csGRP78/α2M* interaction as a novel anti-fibrotic therapeutic intervention for early and late stage DKD, as well as a potential role in non-diabetic CKD. / Thesis / Doctor of Philosophy (Medical Science) / Diabetic kidney disease is the leading cause of kidney failure in developed nations. This progressive disease leads to the loss of kidney function due to an accumulation of scar proteins in the kidney over time. High glucose is a major factor that causes this to occur. Our lab studies specific kidney cells called mesangial cells, proximal tubule epithelial cells, and fibroblasts that produce scar proteins in the presence of high glucose. We have shown that when these cells are treated with high glucose, this causes the movement of a protein called GRP78 that normally resides inside the cell to move to the cell’s surface where it can interact with other proteins. My research has established that the proteins alpha 2-macroglobulin (ɑ2M), integrin β1 (Intβ1), and thrombospondin-1 (TSP1) can bind to GRP78 on the cell surface and cause cells to make scar proteins. Preventing ɑ2M or Intβ1 from binding to GRP78 or preventing TSP1 production prevents mesangial cells from making scar proteins when exposed to high glucose. In a mouse model that overproduces these scar proteins, we showed that preventing cell surface GRP78 and α2M interaction prevents scar protein production and is thus a novel potential treatment option for kidney disease.

Diabetes mellitus is a metabolic disease largely due to lifestyle and nutritional imbalance, resulting in insulin resistance, hyperglycemia and vascular complications. Diabetic kidney disease (DKD) is a major cause of end-stage renal failure contributing to morbidity and mortality worldwide. Therapeutic options to prevent or reverse DKD progression are limited. Endothelial and glomerular filtration barrier (GFB) dysfunction and sterile inflammation are associated with DKD. Neutrophil extracellular traps (NETs), originally identified as an innate immune mechanism to combat infection, have been implicated in sterile inflammatory responses in non-communicable diseases. However, the contribution of NETs in DKD remains unknown. Here, we show that biomarkers of NETs are increased in diabetic mice and diabetic patients and that these changes correlate with DKD severity. Mechanistically, NETs promote NLRP3 inflammasome activation and glomerular endothelial dysfunction under high glucose stress in vitro and in vivo. Inhibition of NETs (PAD4 inhibitor) ameliorate endothelial dysfunction and renal injury in DKD. Taken together, NET-induced sterile inflammation promotes diabetes-associated endothelial dysfunction, identifying a new pathomechanism contributing to DKD. Inhibition of NETs may be a promising therapeutic strategy in DKD.

Contexte : Les patients âgés atteints de néphropathie diabétique ne reçoivent souvent pas un traitement pharmacologique optimal. Les directives de pratique clinique actuelles n'intègrent pas le concept de soins personnalisés. Les algorithmes d'aide à la décision clinique (ADC) qui tiennent compte à la fois des preuves et des soins personnalisés pour améliorer les résultats des patients peuvent améliorer les soins aux personnes âgées. L'objectif de cette recherche est de concevoir et de valider un algorithme ADC pour la prescription d'inhibiteurs du système rénineangiotensine- aldostérone (ISRAA) pour les patients âgés atteints de diabète. Méthodes : La conception de l'algorithme ADC comprenait trois phases principales. Dans la première phase, nous avons recherché, examiné et évalué les preuves actuelles sur plusieurs sujets liés aux décisions de prescription pour les patients âgés et à l'adhésion des cliniciens aux directives de pratique. Nous avons également procédé à un examen systématique et à une méta-analyse d'essais cliniques randomisés afin de déterminer les valeurs du nombre de patients à traiter (NPT) et du délai d'obtention d'un avantage (DOA) applicables à notre population cible en vue de leur utilisation dans l'algorithme. Dans la deuxième phase, nous avons exploré les points de vue des patients et des prestataires de soins de santé sur les outils ADC en menant des entretiens avec les patients et une enquête transversale auprès des prestataires de soins de santé. Dans la troisième et dernière phase, les résultats des études réalisées dans les phases un et deux ont été utilisés pour informer le développement de l'algorithme ADC qui a ensuite été validé dans une étude Delphi. Résultats : Nous avons créé un algorithme ADC qui couvrait 16 scénarios possibles. Neuf scénarios correspondaient à des recommandations de méta-analyses, tandis que cinq scénarios correspondaient à des directives de pratique clinique. Pour les neuf cas, nous avons généré 36 recommandations personnalisées et neuf recommandations générales sur la base des valeurs NPT et DOA calculées et appariées. En outre, nous avons pris en compte l'espérance de vie et la capacité fonctionnelle du patient. L'algorithme a été validé lors de trois tours d'une étude Delphi. Conclusion : Nous avons conçu un algorithme de CDS fondé sur des preuves qui intègre des considérations souvent négligées dans les directives de pratique clinique, notamment l'espérance de vie restante, la charge médicamenteuse et l'état fonctionnel. Les prochaines étapes consistent à le tester dans le cadre d'un essai clinique afin d'étudier s'il est capable d'atteindre des objectifs cliniques prévisibles et réalistes, de maintenir la qualité de vie des personnes âgées et de réduire l'utilisation et le coût du système de santé. / Background: Older patients with diabetic nephropathy often do not receive optimal pharmacological treatment. Current clinical practice guidelines do not incorporate the concept of personalized care. Clinical decision support (CDS) algorithms that consider both evidence and personalized care to improve patient outcomes can improve the care of older adults. The aim of this research is to design and validate a CDS algorithm for prescribing renin-angiotensin aldosterone system inhibitors (RAASi) for older patients with diabetes. Methods: The design of the CDS algorithm included three main phases. In phase one, we searched, reviewed, and evaluated current evidence on several topics related to prescribing decisions for older patients and clinicians' adherence to practice guidelines. We also conducted a systematic review and a meta-analysis of randomized clinical trials to determine the number needed to treat (NNT) and time-to-benefit (TTB) values applicable to our target population for use in the algorithm. In phase two, we explored the views of patients and healthcare providers on CDS tools through conducting patient interviews and a cross-sectional survey of healthcare providers. In the third and final phase, findings from studies completed in phases one and two were used to inform the development of the CDS algorithm which was then validated using modified Delphi methodology. Results: We have created a CDS algorithm that covered 16 possible scenarios. There were nine scenarios matched to meta-analysis recommendations, while five scenarios were matched to clinical practice guidelines. For the nine cases, we have generated 36 personalized and nine general recommendations based on the calculated and matched NNT and TTB values. In addition, we have considered the patient’s life expectancy and functional capacity. The algorithm was validated in three rounds of a modified Delphi study. Conclusion: We designed an evidenceinformed CDS algorithm that integrates considerations often overlooked in clinical practice guidelines, including remaining life expectancy, medication burden, and functional status. The next steps include testing in a clinical trial to study if it is able to achieve predictable and realistic clinical goals, maintaining quality of life in older adults, and reducing healthcare system use and cost.

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