SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID

Partially Specified Nearest Neighbor Search

We study the Partial Nearest Neighbor Problem that consists in preprocessing n points D from d-dimensional metric space such that the following query can be answered efficiently: Given a query vector Q ∈ R d and an axes-aligned query subspace represented by S ∈ {0, 1} d, report a point P ∈ D with dS(Q, P) ≤ dS(Q, P ′ ) for all P ′ ∈ D, where dS(Q, P) is the distance between Q and P in the subspace S. This problem is related to similarity search between feature vectors w.r.t. a subset of features. Thus, the problem is of great practical importance in bioinformatics, image recognition, etc., however, due to exponentially many subspaces, each changing distances significantly, the problem has a considerable complexity. We present the first exact algorithms for ℓ2- and ℓ∞-metrics with linear space and sub-linear worst-case query time. We also give a simple approximation algorithm, and show experimentally that our approach performs well on real world data.