Consistency of metabolite associations with measured glomerular filtration rate in children and adults.

Background: There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation. Methods: We evaluated the consistency of associations between measured GFR (mGFR) and 887 common, known metabolites quantified by an untargeted chromatography- and spectroscopy-based metabolomics platform (Metabolon) performed on frozen blood samples from 580 participants in Chronic Kidney Disease in Children (CKiD), 674 participants in Modification of Diet in Renal Disease (MDRD) Study and 962 participants in African American Study of Kidney Disease and Hypertension (AASK). We evaluated metabolite-mGFR correlation association with metabolite class, molecular weight, assay platform and measurement coefficient of variation (CV). Among metabolites with strong negative correlations with mGFR (r < -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI). Results: A total of 561 metabolites (63%) were negatively correlated with mGFR. Correlations with mGFR were highly consistent across study, sex, race and BMI categories (correlation of metabolite-mGFR correlations between 0.88 and 0.95). Amino acids, carbohydrates and nucleotides were more often negatively correlated with mGFR compared with lipids, but there was no association with metabolite molecular weight, liquid chromatography/mass spectrometry platform and measurement CV. Among 114 metabolites with strong negative associations with mGFR (r < -0.5), 27 were consistently not associated with age (height in children), sex or race. Conclusions: The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.

The Drosophila histone methyl-transferase SET1 coordinates multiple signaling pathways in regulating male germline stem cell maintenance and differentiation.

Many cell types come from tissue-specific adult stem cells that maintain the balance between proliferation and differentiation. Here, we study how the H3K4me3 methyltransferase, Set1, regulates early-stage male germ cell proliferation and differentiation in Drosophila. Early-stage germline-specific knockdown of set1 results in a temporally progressed defects, arising as germ cell loss and developing to overpopulated early-stage germ cells. These germline defects also impact the niche architecture and cyst stem cell lineage in a non-cell-autonomous manner. Additionally, wild-type Set1, but not the catalytically inactive Set1, could rescue the set1 knockdown phenotypes, highlighting the functional importance of the methyl-transferase activity of the Set1 enzyme. Further, RNA-seq experiments reveal key signaling pathway components, such as the JAK-STAT pathway gene stat92E and the BMP pathway gene mad, that are upregulated upon set1 knockdown. Genetic interaction assays support the functional relationships between set1 and JAK-STAT or BMP pathways, as mutations of both the stat92E and mad genes suppress the set1 knockdown phenotypes. These findings enhance our understanding of the balance between proliferation and differentiation in an adult stem cell lineage. The germ cell loss followed by over-proliferation phenotypes when inhibiting a histone methyl-transferase raise concerns about using their inhibitors in cancer therapy.

Differential effects of aneuploidy on growth and differentiation in human intestinal stem cells.

Aneuploidy, a near ubiquitous genetic feature of tumors, is a context-dependent driver of cancer evolution; however, the mechanistic basis of this role remains unclear. Here, by inducing heterogeneous aneuploidy in non-transformed human colon organoids (colonoids), we investigate how the effects of aneuploidy on cell growth and differentiation may promote malignant transformation. Single-cell RNA sequencing reveals that the gene expression signature across over 100 unique aneuploid karyotypes is enriched with p53 responsive genes. The primary driver of p53 activation is karyotype complexity. Complex aneuploid cells with multiple unbalanced chromosomes activate p53 and undergo G1 cell-cycle arrest, independent of DNA damage and without evidence of senescence. By contrast, simple aneuploid cells with 1-3 chromosomes gained or lost continue to proliferate, demonstrated by single cell tracking in colonoids. Notably, simple aneuploid cells exhibit impaired differentiation when niche factors are withdrawn. These findings suggest that while complex aneuploid cells are eliminated from the normal epithelium due to p53 activation, simple aneuploid cells can escape this checkpoint and may contribute to niche factor-independent growth of cancer-initiating cells.

Lung tumor-infiltrating Treg have divergent transcriptional profiles and function linked to checkpoint blockade response.

Regulatory T cells (Treg) are conventionally viewed as suppressors of endogenous and therapy-induced antitumor immunity; however, their role in modulating responses to immune checkpoint blockade (ICB) is unclear. In this study, we integrated single-cell RNA-seq/T cell receptor sequencing (TCRseq) of >73,000 tumor-infiltrating Treg (TIL-Treg) from anti-PD-1-treated and treatment-naive non-small cell lung cancers (NSCLC) with single-cell analysis of tumor-associated antigen (TAA)-specific Treg derived from a murine tumor model. We identified 10 subsets of human TIL-Treg, most of which have high concordance with murine TIL-Treg subsets. Only one subset selectively expresses high levels of TNFRSF4 (OX40) and TNFRSF18 (GITR), whose engangement by cognate ligand mediated proliferative programs and NF-κB activation, as well as multiple genes involved in Treg suppression, including LAG3. Functionally, the OX40hiGITRhi subset is the most highly suppressive ex vivo, and its higher representation among total TIL-Treg correlated with resistance to PD-1 blockade. Unexpectedly, in the murine tumor model, we found that virtually all TIL-Treg-expressing T cell receptors that are specific for TAA fully develop a distinct TH1-like signature over a 2-week period after entry into the tumor, down-regulating FoxP3 and up-regulating expression of TBX21 (Tbet), IFNG, and certain proinflammatory granzymes. Transfer learning of a gene score from the murine TAA-specific TH1-like Treg subset to the human single-cell dataset revealed a highly analogous subcluster that was enriched in anti-PD-1-responding tumors. These findings demonstrate that TIL-Treg partition into multiple distinct transcriptionally defined subsets with potentially opposing effects on ICB-induced antitumor immunity and suggest that TAA-specific TIL-Treg may positively contribute to antitumor responses.

Integrated molecular and clinical analysis of BRAF-mutant glioma in adults.

BRAF mutations are a significant driver of disease in pediatric low-grade glioma, but the implications of BRAF alterations on the clinical course and treatment response in adult glioma remain unclear. Here, we characterize a multi-institutional cohort of more than 300 patients (>200 adults) with BRAF-mutated glioma using clinical, pathological/molecular, and outcome data. We observed that adult and pediatric BRAF-mutant gliomas harbor distinct clinical and molecular features, with a higher prevalence of BRAFV600E (Class I) and BRAF fusions in pediatric tumors. BRAFV600E alterations were associated with improved survival in adults with glioma overall, though not in glioblastoma. Other genomic alterations observed within functional classes were consistent with the putative roles of those BRAF mutation classes in glioma pathogenesis. In our adult cohort, BRAFV600E alterations conferred sensitivity to targeted therapies. Overall, this large cohort of BRAF-altered adult gliomas demonstrates a broad range of molecular alterations with implications for treatment sensitivity and survival.

Integrated cell-free DNA and cytokine analysis uncovers distinct tissue injury and immune response patterns in solid organ transplant recipients with COVID-19.

COVID-19 pathogenesis is associated with an exuberant inflammatory response. However, the tissue injury pattern and immune response in solid-organ transplant recipients (SOTRs) taking immunosuppressive therapy have not been well characterized. Here, we perform both cfDNA and cytokine profiling on plasma samples to map tissue damage, including allograft injury and delineate underlying immunopathology. We identified injuries from multiple-tissue types, including hematopoietic cells, vascular endothelium, hepatocyte, adipocyte, pancreas, kidney, heart, and lung in SOTRs with COVID-19 that correlates with disease severity. SOTRs with COVID-19 have higher plasma levels of cytokines such as IFN-λ1, IFN-γ, IL-15, IL-18 IL-1RA, IL-6, MCP-2, and TNF-α as compared to healthy controls, and the levels of GM-CSF, IL-15, IL-6, IL-8, and IL-10 were associated with disease severity in SOTRs. Strikingly, IFN-λ and IP-10 were markedly increased in SOTRs compared to immunocompetent patients with COVID-19. Correlation analyses showed a strong association between monocyte-derived cfDNA and inflammatory cytokines/chemokines in SOTRs with COVID-19. Moreover, compared to other respiratory viral infections, COVID-19 induced pronounced injury in hematopoietic, vascular endothelial and endocrine tissues. Allograft injury, measured as donor-derived cfDNA was elevated in SOTRs with COVID-19, including allografts distant from the primary site of infection. Allograft injury correlated with inflammatory cytokines and cfDNA from immune cells. Furthermore, longitudinal analysis identified a gradual decrease of cfDNA and inflammatory cytokine levels in patients with a favorable outcome. Our findings highlight distinct tissue injury and cytokine features in SOTRs with COVID-19 that correlate with disease severity.

Colonic epithelial adaptation to EGFR-independent growth induces chromosomal instability and is accelerated by prior injury.

Although much is known about the gene mutations required to drive colorectal cancer (CRC) initiation, the tissue-specific selective microenvironments in which neoplasia arises remains less characterized. Here, we determined whether modulation of intestinal stem cell niche morphogens alone can exert a neoplasia-relevant selective pressure on normal colonic epithelium. Using adult stem cell-derived murine colonic epithelial organoids (colonoids), we employed a strategy of sustained withdrawal of epidermal growth factor (EGF) and epidermal growth factor receptor (EGFR) inhibition to select for and expand survivors. EGFR-signaling-independent (iEGFR) colonoids emerged over rounds of selection and expansion. Colonoids derived from a mouse model of chronic mucosal injury showed an enhanced ability to adapt to EGFR inhibition. Whole-exome and transcriptomic analyses of iEGFR colonoids demonstrated acquisition of deleterious mutations and altered expression of genes implicated in EGF signaling, pyroptosis, and CRC. iEGFR colonoids acquired dysplasia-associated cytomorphologic changes, an increased proliferative rate, and the ability to survive independently of other required niche factors. These changes were accompanied by emergence of aneuploidy and chromosomal instability; further, the observed mitotic segregation errors were significantly associated with loss of interkinetic nuclear migration, a fundamental and dynamic process underlying intestinal epithelial homeostasis. This study provides key evidence that chromosomal instability and other phenotypes associated with neoplasia can be induced ex vivo via adaptation to EGF withdrawal in normal and stably euploid colonic epithelium, without introducing cancer-associated driver mutations. In addition, prior mucosal injury accelerates this evolutionary process.

Training a computer-aided polyp detection system to detect sessile serrated adenomas using public domain colonoscopy videos.

Background Colorectal cancer (CRC) is a major public health burden worldwide, and colonoscopy is the most commonly used CRC screening tool. Still, there is variability in adenoma detection rate (ADR) among endoscopists. Recent studies have reported improved ADR using deep learning models trained on videos curated largely from private in-house datasets. Few have focused on the detection of sessile serrated adenomas (SSAs), which are the most challenging target clinically. Methods We identified 23 colonoscopy videos available in the public domain and for which pathology data were provided, totaling 390 minutes of footage. Expert endoscopists annotated segments of video with adenomatous polyps, from which we captured 509 polyp-positive and 6,875 polyp-free frames. Via data augmentation, we generated 15,270 adenomatous polyp-positive images, of which 2,310 were SSAs, and 20,625 polyp-negative images. We used the CNN AlexNet and fine-tuned its parameters using 90 % of the images, before testing its performance on the remaining 10 % of images unseen by the model. Results We trained the model on 32,305 images and tested performance on 3,590 images with the same proportion of SSA, non-SSA polyp-positive, and polyp-negative images. The overall accuracy of the model was 0.86, with a sensitivity of 0.73 and a specificity of 0.96. Positive predictive value was 0.93 and negative predictive value was 0.96. The area under the curve was 0.94. SSAs were detected in 93 % of SSA-positive images. Conclusions Using a relatively small set of publicly-available colonoscopy data, we obtained sizable training and validation sets of endoscopic images using data augmentation, and achieved an excellent performance in adenomatous polyp detection.

TCF12 haploinsufficiency causes autosomal dominant Kallmann syndrome and reveals network-level interactions between causal loci.

Dysfunction of the gonadotropin-releasing hormone (GnRH) axis causes a range of reproductive phenotypes resulting from defects in the specification, migration and/or function of GnRH neurons. To identify additional molecular components of this system, we initiated a systematic genetic interrogation of families with isolated GnRH deficiency (IGD). Here, we report 13 families (12 autosomal dominant and one autosomal recessive) with an anosmic form of IGD (Kallmann syndrome) with loss-of-function mutations in TCF12, a locus also known to cause syndromic and non-syndromic craniosynostosis. We show that loss of tcf12 in zebrafish larvae perturbs GnRH neuronal patterning with concomitant attenuation of the orthologous expression of tcf3a/b, encoding a binding partner of TCF12, and stub1, a gene that is both mutated in other syndromic forms of IGD and maps to a TCF12 affinity network. Finally, we report that restored STUB1 mRNA rescues loss of tcf12 in vivo. Our data extend the mutational landscape of IGD, highlight the genetic links between craniofacial patterning and GnRH dysfunction and begin to assemble the functional network that regulates the development of the GnRH axis.

Deep Learning and Transfer Learning for Optic Disc Laterality Detection: Implications for Machine Learning in Neuro-Ophthalmology.

BACKGROUND: Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. METHODS: Using transfer learning, we modified the ResNet-152 deep convolutional neural network (DCNN), pretrained on ImageNet, to determine the optic disc laterality. After a 5-fold cross-validation, we generated receiver operating characteristic curves and corresponding area under the curve (AUC) values to evaluate performance. The data set consisted of 576 color fundus photographs (51% right and 49% left). Both 30° photographs centered on the optic disc (63%) and photographs with varying degree of optic disc centration and/or wider field of view (37%) were included. Both normal (27%) and abnormal (73%) optic discs were included. Various neuro-ophthalmological diseases were represented, such as, but not limited to, atrophy, anterior ischemic optic neuropathy, hypoplasia, and papilledema. RESULTS: Using 5-fold cross-validation (70% training; 10% validation; 20% testing), our DCNN for classifying right vs left optic disc achieved an average AUC of 0.999 (±0.002) with optimal threshold values, yielding an average accuracy of 98.78% (±1.52%), sensitivity of 98.60% (±1.72%), and specificity of 98.97% (±1.38%). When tested against a separate data set for external validation, our 5-fold cross-validation model achieved the following average performance: AUC 0.996 (±0.005), accuracy 97.2% (±2.0%), sensitivity 96.4% (±4.3%), and specificity 98.0% (±2.2%). CONCLUSIONS: Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.

Dissecting limiting factors of the Protein synthesis Using Recombinant Elements (PURE) system.

Reconstituted cell-free protein synthesis systems such as the Protein synthesis Using Recombinant Elements (PURE) system give high-throughput and controlled access to in vitro protein synthesis. Here we show that compared with the commercial S30 crude extract based RTS 100 E. coli HY system, the PURE system has less mRNA degradation and produces up to ∼6-fold full-length proteins. However the majority of polypeptides PURE produces are partially translated or inactive since the signal from firefly luciferase (Fluc) translated in PURE is only ∼2/3rd of that measured using the RTS 100 E. coli HY S30 system. Both of the 2 batch systems suffer from low ribosome recycling efficiency when translating proteins from 82 kD to 224 kD. A systematic fed-batch analysis of PURE shows replenishment of 6 small molecule substrates individually or in combination before energy depletion increased Fluc protein yield by ∼1.5 to ∼2-fold, while creatine phosphate and magnesium have synergistic effects when added to the PURE system. Additionally, while adding EF-P to PURE reduced full-length protein translated, it increased the fraction of functional protein and reduced partially translated protein probably by slowing down the translation process. Finally, ArfA, rather than YaeJ or PrfH, helped reduce ribosome stalling when translating Fluc and improved system productivity in a template-dependent fashion.

A scored human protein-protein interaction network to catalyze genomic interpretation.

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.