Transcriptome analysis of proximal tubular cells (HK-2) exposed to urines of type 1 diabetes patients at risk of early progressive renal function decline

Inwardly rectifying potassium channels mediate polymyxin-induced nephrotoxicity

Polymyxin antibiotics are often used as a last-line defense to treat life-threatening Gram-negative pathogens. However, polymyxin-induced kidney toxicity is a dose-limiting factor of paramount importance and can lead to suboptimal treatment. To elucidate the mechanism and develop effective strategies to overcome polymyxin toxicity, we employed a whole-genome CRISPR screen in human kidney tubular HK-2 cells and identified 86 significant genes that upon knock-out rescued polymyxin-induced toxicity. Specifically, we discovered that knockout of the inwardly rectifying potassium channels Kir4.2 and Kir5.1 (encoded by KCNJ15 and KCNJ16, respectively) rescued polymyxin-induced toxicity in HK-2 cells. Furthermore, we found that polymyxins induced cell depolarization via Kir4.2 and Kir5.1 and a significant cellular uptake of polymyxins was evident. All-atom molecular dynamics simulations revealed that polymyxin B₁ spontaneously bound to Kir4.2, thereby increasing opening of the channel, resulting in a potassium influx, and changes of the membrane potential. Consistent with these findings, small molecule inhibitors (BaCl₂ and VU0134992) of Kir potassium channels reduced polymyxin-induced toxicity in cell culture and mouse explant kidney tissue. Our findings provide critical mechanistic information that will help attenuate polymyxin-induced nephrotoxicity in patients and facilitate the design of novel, safer polymyxins.

Single-cell transcriptomics unveils gene regulatory network plasticity

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.

RESULTS

We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.

CONCLUSIONS

Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.

Omics databases on kidney disease: where they can be found and how to benefit from them

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

Progressive renal decline as the major feature of diabetic nephropathy in type 1 diabetes

Despite almost universal implementation of renoprotective therapies over the past 25 years, the risk of end-stage renal disease (ESRD) in type 1 diabetes (T1D) is not decreasing, and ESRD remains the major cause of excess morbidity and premature mortality [1]. Such a state of affairs prompts a call to action. In this review we re-evaluated the proteinuria-centric model of diabetic nephropathy and showed its deficiencies. On the basis of extensive studies that we have been conducting on the patients attending the Joslin Clinic, we propose that progressive renal decline, not abnormalities in urinary albumin excretion, should be considered as the major feature of disease processes leading to ESRD in T1D. The etiology of diabetic nephropathy should be reconsidered in light of our new findings so our perspective can be broadened regarding new therapeutic targets available for interrupting the progressive renal decline in T1D. Reduction in the loss of glomerular filtration rate, not reduction of albumin excretion rate, should become the measure for evaluating the effectiveness of new therapeutic interventions. We need new accurate methods for early diagnosis of patients at risk of progressive renal decline or, better still, for detecting in advance which patients will have rapid, moderate or minimal rate of progression to ESRD.