An atlas of healthy and injured cell states and niches in the human kidney.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.

Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Codetta: predicting the genetic code from nucleotide sequence.

SUMMARY: Codetta is a Python program for predicting the genetic code table of an organism from nucleotide sequences. Codetta can analyze an arbitrary nucleotide sequence and needs no sequence annotation or taxonomic placement. The most likely amino acid decoding for each of the 64 codons is inferred from alignments of profile hidden Markov models of conserved proteins to the input sequence. AVAILABILITY AND IMPLEMENTATION: Codetta 2.0 is implemented as a Python 3 program for MacOS and Linux and is available from http://eddylab.org/software/codetta/codetta2.tar.gz and at http://github.com/kshulgina/codetta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction: Constructing Benchmark Test Sets for Biological Sequence Analysis Using Independent Set Algorithms.

[This corrects the article DOI: 10.1371/journal.pcbi.1009492.].

ORFeus: a computational method to detect programmed ribosomal frameshifts and other non-canonical translation events.

BACKGROUND: In canonical protein translation, ribosomes initiate translation at a specific start codon, maintain a single reading frame throughout elongation, and terminate at the first in-frame stop codon. However, ribosomal behavior can deviate at each of these steps, sometimes in a programmed manner. Certain mRNAs contain sequence and structural elements that cause ribosomes to begin translation at alternative start codons, shift reading frame, read through stop codons, or reinitiate on the same mRNA. These processes represent important translational control mechanisms that can allow an mRNA to encode multiple functional protein products or regulate protein expression. The prevalence of these events remains uncertain, due to the difficulty of systematic detection. RESULTS: We have developed a computational model to infer non-canonical translation events from ribosome profiling data. CONCLUSION: ORFeus identifies known examples of alternative open reading frames and recoding events across different organisms and enables transcriptome-wide searches for novel events.

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis.

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Many, but not all, lineage-specific genes can be explained by homology detection failure.

Genes for which homologs can be detected only in a limited group of evolutionarily related species, called "lineage-specific genes," are pervasive: Essentially every lineage has them, and they often comprise a sizable fraction of the group's total genes. Lineage-specific genes are often interpreted as "novel" genes, representing genetic novelty born anew within that lineage. Here, we develop a simple method to test an alternative null hypothesis: that lineage-specific genes do have homologs outside of the lineage that, even while evolving at a constant rate in a novelty-free manner, have merely become undetectable by search algorithms used to infer homology. We show that this null hypothesis is sufficient to explain the lack of detected homologs of a large number of lineage-specific genes in fungi and insects. However, we also find that a minority of lineage-specific genes in both clades are not well explained by this novelty-free model. The method provides a simple way of identifying which lineage-specific genes call for special explanations beyond homology detection failure, highlighting them as interesting candidates for further study.

Constructing benchmark test sets for biological sequence analysis using independent set algorithms.

Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

The association of low birthweight and prematurity on outcomes in children and adults with nephrotic syndrome-a NEPTUNE cohort study.

BACKGROUND: In single-center studies, both preterm birth and low birth weight (LBW) are associated with worse outcomes in childhood nephrotic syndrome. Using the Nephrotic Syndrome Study Network (NEPTUNE) observational cohort, we tested the hypothesis that in patients with nephrotic syndrome, hypertension, proteinuria status, and disease progression would be more prevalent and more severe in subjects with LBW and prematurity singly or in combination (LBW/prematurity). METHODS: Three hundred fifty-nine adults and children with focal segmental glomerulosclerosis (FSGS) or minimal change disease (MCD) and available birth history were included. Estimated glomerular filtration rate (eGFR) decline and remission status were primary outcomes, and secondary outcomes were kidney histopathology, kidney gene expression, and urinary biomarkers. Logistic regression was used to identify associations with LBW/prematurity and these outcomes. RESULTS: We did not find an association between LBW/prematurity and remission of proteinuria. However, LBW/prematurity was associated with greater decline in eGFR. This decline in eGFR was partially explained by the association of LBW/prematurity with APOL1 high-risk alleles, but the association remained after adjustment. There were no differences in kidney histopathology or gene expression in the LBW/prematurity group compared to normal birth weight/term birth. CONCLUSION: LBW and premature babies who develop nephrotic syndrome have a more rapid decline in kidney function. We did not identify clinical or laboratory features that distinguished the groups. Additional studies in larger groups are needed to fully ascertain the effects of (LBW) and prematurity alone or in combination on kidney function in the setting of nephrotic syndrome.

Rfam 14: expanded coverage of metagenomic, viral and microRNA families.

Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

Activation of Stimulator of IFN Genes (STING) Causes Proteinuria and Contributes to Glomerular Diseases.

BACKGROUND: The signaling molecule stimulator of IFN genes (STING) was identified as a crucial regulator of the DNA-sensing cyclic GMP-AMP synthase (cGAS)-STING pathway, and this signaling pathway regulates inflammation and energy homeostasis under conditions of obesity, kidney fibrosis, and AKI. However, the role of STING in causing CKD, including diabetic kidney disease (DKD) and Alport syndrome, is unknown. METHODS: To investigate whether STING activation contributes to the development and progression of glomerular diseases such as DKD and Alport syndrome, immortalized human and murine podocytes were differentiated for 14 days and treated with a STING-specific agonist. We used diabetic db/db mice, mice with experimental Alport syndrome, C57BL/6 mice, and STING knockout mice to assess the role of the STING signaling pathway in kidney failure. RESULTS: In vitro, murine and human podocytes express all of the components of the cGAS-STING pathway. In vivo, activation of STING renders C57BL/6 mice susceptible to albuminuria and podocyte loss. STING is activated at baseline in mice with experimental DKD and Alport syndrome. STING activation occurs in the glomerular but not the tubulointerstitial compartment in association with autophagic podocyte death in Alport syndrome mice and with apoptotic podocyte death in DKD mouse models. Genetic or pharmacologic inhibition of STING protects from progression of kidney disease in mice with DKD and Alport syndrome and increases lifespan in Alport syndrome mice. CONCLUSION: The activation of the STING pathway acts as a mediator of disease progression in DKD and Alport syndrome. Targeting STING may offer a therapeutic option to treat glomerular diseases of metabolic and nonmetabolic origin or prevent their development, progression, or both.

Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome.

RATIONALE & OBJECTIVE: The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies nor the clinical and molecular heterogeneity of these diseases. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 221 MCD and FSGS patients enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). EXPOSURE: The NEPTUNE Digital Pathology Scoring System (NDPSS) was applied to generate scores for 37 glomerular descriptors. OUTCOME: Time from biopsy to complete proteinuria remission, time from biopsy to kidney disease progression (40% estimated glomerular filtration rate [eGFR] decline or kidney failure), and eGFR over time. ANALYTICAL APPROACH: Cluster analysis was used to group patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for associations with outcomes using adjusted Cox models and linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes between clusters and identify genes associated with individual descriptors driving cluster membership. RESULTS: Three clusters were identified: X (n = 56), Y (n = 68), and Z (n = 97). Clusters Y and Z had higher probabilities of proteinuria remission (HRs of 1.95 [95% CI, 0.99-3.85] and 3.29 [95% CI, 1.52-7.13], respectively), lower hazards of disease progression (HRs of 0.22 [95% CI, 0.08-0.57] and 0.11 [95% CI, 0.03-0.45], respectively), and lower loss of eGFR over time compared with X. Cluster X had 1,920 genes that were differentially expressed compared with Y+Z; these reflected activation of pathways of immune response and inflammation. Six descriptors driving the clusters individually correlated with clinical outcomes and gene expression. LIMITATIONS: Low prevalence of some descriptors and biopsy at a single time point. CONCLUSIONS: The NDPSS allows for categorization of FSGS/MCD patients into clinically and biologically relevant subgroups, and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems.

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects.

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome-wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS-/- mice receiving renin-angiotensin-aldosterone system (RAS)-blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self-organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro-inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.

Annexin A1 alleviates kidney injury by promoting the resolution of inflammation in diabetic nephropathy.

Since failed resolution of inflammation is a major contributor to the progression of diabetic nephropathy, identifying endogenously generated molecules that promote the physiological resolution of inflammation may be a promising therapeutic approach for this disease. Annexin A1 (ANXA1), as an endogenous mediator, plays an important role in resolving inflammation. Whether ANXA1 could affect established diabetic nephropathy through modulating inflammatory states remains largely unknown. In the current study, we found that in patients with diabetic nephropathy, the levels of ANXA1 were upregulated in kidneys, and correlated with kidney function as well as kidney outcomes. Therefore, the role of endogenous ANXA1 in mouse models of diabetic nephropathy was further evaluated. ANXA1 deficiency exacerbated kidney injuries, exhibiting more severe albuminuria, mesangial matrix expansion, tubulointerstitial lesions, kidney inflammation and fibrosis in high fat diet/streptozotocin-induced-diabetic mice. Consistently, ANXA1 overexpression ameliorated kidney injuries in mice with diabetic nephropathy. Additionally, we found Ac2-26 (an ANXA1 mimetic peptide) had therapeutic potential for alleviating kidney injuries in db/db mice and diabetic Anxa1 knockout mice. Mechanistic studies demonstrated that intracellular ANXA1 bound to the transcription factor NF-κB p65 subunit, inhibiting its activation thereby modulating the inflammatory state. Thus, our data indicate that ANXA1 may be a promising therapeutic approach to treating and reversing diabetic nephropathy.

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome.

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

Estimating the power of sequence covariation for detecting conserved RNA structure.

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long non-coding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures. AVAILABILITY AND IMPLEMENTATION: The R-scape web server is at eddylab.org/R-scape, with a link to download the source code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Remote homology search with hidden Potts models.

Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA structure homology search benchmarks, where we can compare directly to exact alignment methods that capture nested RNA base-pairing correlations (stochastic context-free grammars). HPMs perform promisingly in these proof of principle experiments.

The Pfam protein families database in 2019.

The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.