Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease.

Intestinal mesenchymal cells play essential roles in epithelial homeostasis, matrix remodeling, immunity, and inflammation. But the extent of heterogeneity within the colonic mesenchyme in these processes remains unknown. Using unbiased single-cell profiling of over 16,500 colonic mesenchymal cells, we reveal four subsets of fibroblasts expressing divergent transcriptional regulators and functional pathways, in addition to pericytes and myofibroblasts. We identified a niche population located in proximity to epithelial crypts expressing SOX6, F3 (CD142), and WNT genes essential for colonic epithelial stem cell function. In colitis, we observed dysregulation of this niche and emergence of an activated mesenchymal population. This subset expressed TNF superfamily member 14 (TNFSF14), fibroblastic reticular cell-associated genes, IL-33, and Lysyl oxidases. Further, it induced factors that impaired epithelial proliferation and maturation and contributed to oxidative stress and disease severity in vivo. Our work defines how the colonic mesenchyme remodels to fuel inflammation and barrier dysfunction in IBD.

Adipocyte autophagy limits gut inflammation by controlling oxylipin and IL-10.

Lipids play a major role in inflammatory diseases by altering inflammatory cell functions, either through their function as energy substrates or as lipid mediators such as oxylipins. Autophagy, a lysosomal degradation pathway that limits inflammation, is known to impact on lipid availability, however, whether this controls inflammation remains unexplored. We found that upon intestinal inflammation visceral adipocytes upregulate autophagy and that adipocyte-specific loss of the autophagy gene Atg7 exacerbates inflammation. While autophagy decreased lipolytic release of free fatty acids, loss of the major lipolytic enzyme Pnpla2/Atgl in adipocytes did not alter intestinal inflammation, ruling out free fatty acids as anti-inflammatory energy substrates. Instead, Atg7-deficient adipose tissues exhibited an oxylipin imbalance, driven through an NRF2-mediated upregulation of Ephx1. This shift reduced secretion of IL-10 from adipose tissues, which was dependent on the cytochrome P450-EPHX pathway, and lowered circulating levels of IL-10 to exacerbate intestinal inflammation. These results suggest an underappreciated fat-gut crosstalk through an autophagy-dependent regulation of anti-inflammatory oxylipins via the cytochrome P450-EPHX pathway, indicating a protective effect of adipose tissues for distant inflammation.

Autophagy-Dependent Generation of Free Fatty Acids Is Critical for Normal Neutrophil Differentiation.

Neutrophils are critical and short-lived mediators of innate immunity that require constant replenishment. Their differentiation in the bone marrow requires extensive cytoplasmic and nuclear remodeling, but the processes governing these energy-consuming changes are unknown. While previous studies show that autophagy is required for differentiation of other blood cell lineages, its function during granulopoiesis has remained elusive. Here, we have shown that metabolism and autophagy are developmentally programmed and essential for neutrophil differentiation in vivo. Atg7-deficient neutrophil precursors had increased glycolytic activity but impaired mitochondrial respiration, decreased ATP production, and accumulated lipid droplets. Inhibiting autophagy-mediated lipid degradation or fatty acid oxidation alone was sufficient to cause defective differentiation, while administration of fatty acids or pyruvate for mitochondrial respiration rescued differentiation in autophagy-deficient neutrophil precursors. Together, we show that autophagy-mediated lipolysis provides free fatty acids to support a mitochondrial respiration pathway essential to neutrophil differentiation.

Cellular iron governs the host response to malaria.

Malaria and iron deficiency are major global health problems with extensive epidemiological overlap. Iron deficiency-induced anaemia can protect the host from malaria by limiting parasite growth. On the other hand, iron deficiency can significantly disrupt immune cell function. However, the impact of host cell iron scarcity beyond anaemia remains elusive in malaria. To address this, we employed a transgenic mouse model carrying a mutation in the transferrin receptor (TfrcY20H/Y20H), which limits the ability of cells to internalise iron from plasma. At homeostasis TfrcY20H/Y20H mice appear healthy and are not anaemic. However, TfrcY20H/Y20H mice infected with Plasmodium chabaudi chabaudi AS showed significantly higher peak parasitaemia and body weight loss. We found that TfrcY20H/Y20H mice displayed a similar trajectory of malaria-induced anaemia as wild-type mice, and elevated circulating iron did not increase peak parasitaemia. Instead, P. chabaudi infected TfrcY20H/Y20H mice had an impaired innate and adaptive immune response, marked by decreased cell proliferation and cytokine production. Moreover, we demonstrated that these immune cell impairments were cell-intrinsic, as ex vivo iron supplementation fully recovered CD4+ T cell and B cell function. Despite the inhibited immune response and increased parasitaemia, TfrcY20H/Y20H mice displayed mitigated liver damage, characterised by decreased parasite sequestration in the liver and an attenuated hepatic immune response. Together, these results show that host cell iron scarcity inhibits the immune response but prevents excessive hepatic tissue damage during malaria infection. These divergent effects shed light on the role of iron in the complex balance between protection and pathology in malaria.

The Preprint Club: A blueprint for community-based peer review: A blueprint for community-based peer review.

Cross-institutional journal clubs focused on preprints are a new approach to community-based peer review and allow ERCs to gain experience.

A Deep Intronic PKHD1 Variant Identified by SpliceAI in a Deceased Neonate With Autosomal Recessive Polycystic Kidney Disease.

The etiologies of newborn deaths in neonatal intensive care units usually remain unknown, even after genetic testing. Whole-genome sequencing, combined with artificial intelligence-based methods for predicting the effects of non-coding variants, provide an avenue for resolving these deaths. Using one such method, SpliceAI, we identified a maternally inherited deep intronic PKHD1 splice variant (chr6:52030169T>C), in trans with a pathogenic missense variant (p.Thr36Met), in a newborn who died of autosomal recessive polycystic kidney disease at age 2 days. We validated the deep intronic variant's impact in maternal urine-derived cells expressing PKHD1. Reverse transcription polymerase chain reaction followed by Sanger sequencing showed that the variant causes inclusion of 147bp of the canonical intron between exons 29 and 30 of PKHD1 into the mRNA, including a premature stop codon. Allele-specific expression analysis at a heterozygous site in the mother showed that the mutant allele completely suppresses canonical splicing. In an unrelated healthy control, there was no evidence of transcripts including the novel splice junction. We returned a diagnostic report to the parents, who underwent in vitro embryo selection.

Small open reading frames: a comparative genetics approach to validation.

Open reading frames (ORFs) with fewer than 100 codons are generally not annotated in genomes, although bona fide genes of that size are known. Newer biochemical studies have suggested that thousands of small protein-coding ORFs (smORFs) may exist in the human genome, but the true number and the biological significance of the micropeptides they encode remain uncertain. Here, we used a comparative genomics approach to identify high-confidence smORFs that are likely protein-coding. We identified 3,326 high-confidence smORFs using constraint within human populations and evolutionary conservation as additional lines of evidence. Next, we validated that, as a group, our high-confidence smORFs are conserved at the amino-acid level rather than merely residing in highly conserved non-coding regions. Finally, we found that high-confidence smORFs are enriched among disease-associated variants from GWAS. Overall, our results highlight that smORF-encoded peptides likely have important functional roles in human disease.

Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles.

Although DNA methylation is the best characterized epigenetic mark, the mechanism by which it is targeted to specific regions in the genome remains unclear. Recent studies have revealed that local DNA methylation profiles might be dictated by cis-regulatory DNA sequences that mainly operate via DNA-binding factors. Consistent with this finding, we have recently shown that disruption of CTCF-binding sites by rare single nucleotide variants (SNVs) can underlie cis-linked DNA methylation changes in patients with congenital anomalies. These data raise the hypothesis that rare genetic variation at transcription factor binding sites (TFBSs) might contribute to local DNA methylation patterning. In this work, by combining blood genome-wide DNA methylation profiles, whole genome sequencing-derived SNVs from 247 unrelated individuals along with 133 predicted TFBS motifs derived from ENCODE ChIP-Seq data, we observed an association between the disruption of binding sites for multiple TFs by rare SNVs and extreme DNA methylation values at both local and, to a lesser extent, distant CpGs. While the majority of these changes affected only single CpGs, 24% were associated with multiple outlier CpGs within ±1kb of the disrupted TFBS. Interestingly, disruption of functionally constrained sites within TF motifs lead to larger DNA methylation changes at nearby CpG sites. Altogether, these findings suggest that rare SNVs at TFBS negatively influence TF-DNA binding, which can lead to an altered local DNA methylation profile. Furthermore, subsequent integration of DNA methylation and RNA-Seq profiles from cardiac tissues enabled us to observe an association between rare SNV-directed DNA methylation and outlier expression of nearby genes. In conclusion, our findings not only provide insights into the effect of rare genetic variation at TFBS on shaping local DNA methylation and its consequences on genome regulation, but also provide a rationale to incorporate DNA methylation data to interpret the functional role of rare variants.

Evaluation and Improvement of the Stability of Poly(ethylene oxide)-based Solid-state Batteries with High-Voltage Cathodes.

Solid-state batteries (SSBs) with high-voltage cathode active materials (CAMs) such as LiNi1-x-y Cox Mny O2 (NCM) and poly(ethylene oxide) (PEO) suffer from "noisy voltage" related cell failure. Moreover, reports on their long-term cycling performance with high-voltage CAMs are not consistent. In this work, we verified that the penetration of lithium dendrites through the solid polymer electrolyte (SPE) indeed causes such "noisy voltage cell failure". This problem can be overcome by a simple modification of the SPE using higher molecular weight PEO, resulting in an improved cycling stability compared to lower molecular weight PEO. Furthermore, X-ray photoelectron spectroscopy analysis confirms the formation of oxidative degradation products after cycling with NCM, for what Fourier transform infrared spectroscopy is not suitable as an analytical technique due to its limited surface sensitivity. Overall, our results help to critically evaluate and improve the stability of PEO-based SSBs.

Interface Design Enabling Stable Polymer/Thiophosphate Electrolyte Separators for Dendrite-Free Lithium Metal Batteries.

Organic/inorganic interfaces greatly affect Li+ transport in composite solid electrolytes (SEs), while SE/electrode interfacial stability plays a critical role in the cycling performance of solid-state batteries (SSBs). However, incomplete understanding of interfacial (in)stability hinders the practical application of composite SEs in SSBs. Herein, chemical degradation between Li6 PS5 Cl (LPSCl) and poly(ethylene glycol) (PEG) is revealed. The high polarity of PEG changes the electronic state and structural bonding of the PS4 3- tetrahedra, thus triggering a series of side reactions. A substituted terminal group of PEG not only stabilizes the inner interfaces but also extends the electrochemical window of the composite SE. Moreover, a LiF-rich layer can effectively prevent side reactions at the Li/SE interface. The results provide insights into the chemical stability of polymer/sulfide composites and demonstrate an interface design to achieve dendrite-free lithium metal batteries.

Physicochemical Concepts of the Lithium Metal Anode in Solid-State Batteries.

Developing reversible lithium metal anodes with high rate capability is one of the central aims of current battery research. Lithium metal anodes are not only required for the development of innovative cell concepts such as lithium-air or lithium-sulfur batteries, they can also increase the energy density of batteries with intercalation-type cathodes. The use of solid electrolyte separators is especially promising to develop well-performing lithium metal anodes, because they can act as a mechanical barrier to avoid unwanted dendritic growth of lithium through the cell. However, inhomogeneous electrodeposition and contact loss often hinder the application of a lithium metal anode in solid-state batteries. In this review, we assess the physicochemical concepts that describe the fundamental mechanisms governing lithium metal anode performance in combination with inorganic solid electrolytes. In particular, our discussion of kinetic rate limitations and morphological stability intends to stimulate further progress in the field of lithium metal anodes.

AKI in Hospitalized Patients with COVID-19.

BACKGROUND: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. METHODS: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. RESULTS: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. CONCLUSIONS: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.

Deep learning and the electrocardiogram: review of the current state-of-the-art.

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

Trends in length of stay for Neonatal Intensive Care Unit patients who die before hospital discharge.

OBJECTIVES: The objectives of this study were to establish days between birth and death for neonates over a 14-year period, determine if days between birth and death have changed over time across gestational age cohorts, and identify diagnoses which may put infants at high risk of prolonged hospitalization leading to death. STUDY DESIGN: This was a single-site, retrospective chart review of inborn infants who died prior to hospital discharge. RESULTS: Two hundred and thirty-nine patients born between 1/1/2006 and 12/31/2020 met inclusion criteria. Days until death ranged from 0 to 300 with a median of 6 days (interquartile range = 23). Median days until death increased over time, with a statistically significant increase between epoch 1 and epoch 2 (p = 0.016) but not between epoch 2 and epoch 3 (p = 0.618). Extremely premature infants died earlier than more mature infants (p < 0.001). In addition, infants with complex congenital heart disease or a gastrointestinal (GI) catastrophe died later (p < 0.001 and p < 0.001, respectively) than newborns without cardiac or GI issues. CONCLUSIONS: Our findings demonstrate an increase in time to death for newborns who did not survive to hospital discharge over a 14-year period. This trend suggests that the dynamics informing Meadows' assertion that "doomed infants die early" may be shifting, with some seriously ill infants who die before hospital discharge surviving longer than previously described. More research is needed to understand how best to care for babies who will not survive to discharge and to explore when supports such as palliative care consultation may be beneficial. KEY POINTS: As per W. Meadow, "Doomed infants die early" · Pre-death length of stay varies with diagnosis.. · Some seriously ill infants who die before hospital discharge are no longer dying early.. · These infants and families may need supports..

Elucidation of de novo small insertion/deletion biology with parent-of-origin phasing.

The mechanisms underlying de novo insertion/deletion (indel) genesis, such as polymerase slippage, have been hypothesized but not well characterized in the human genome. We implemented two methodological improvements, which were leveraged to dissect indel mutagenesis. We assigned de novo variants to parent-of-origin (i.e., phasing) with low-coverage long-read whole-genome sequencing, achieving better phasing compared to short-read sequencing (medians of 84% and 23%, respectively). We then wrote an application programming interface to classify indels into three subtypes according to sequence context. Across three cohorts with different phasing methods (Ntrios = 540, all cohorts), we observed that one de novo indel subtype, change in copy count (CCC), was significantly correlated with father's (p = 7.1 × 10-4 ) but not mother's (p = .45) age at conception. We replicated this effect in three cohorts without de novo phasing (ppaternal = 1.9 × 10-9 , pmaternal = .61; Ntrios = 3,391, all cohorts). Although this is consistent with polymerase slippage during spermatogenesis, the percentage of variance explained by paternal age was low, and we did not observe an association with replication timing. These results suggest that spermatogenesis-specific events have a minor role in CCC indel mutagenesis, one not observed for other indel subtypes nor for maternal age in general. These results have implications for indel modeling in evolution and disease.

Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City.

BACKGROUND: There are limited data regarding the clinical impact of coronavirus disease 2019 (COVID-19) on people living with human immunodeficiency virus (PLWH). In this study, we compared outcomes for PLWH with COVID-19 to a matched comparison group. METHODS: We identified 88 PLWH hospitalized with laboratory-confirmed COVID-19 in our hospital system in New York City between 12 March and 23 April 2020. We collected data on baseline clinical characteristics, laboratory values, HIV status, treatment, and outcomes from this group and matched comparators (1 PLWH to up to 5 patients by age, sex, race/ethnicity, and calendar week of infection). We compared clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these groups, as well as cumulative incidence of death by HIV status. RESULTS: Patients did not differ significantly by HIV status by age, sex, or race/ethnicity due to the matching algorithm. PLWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PLWH had greater proportions of smoking (P < .001) and comorbid illness than uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (P = .15). Poor outcomes for hospitalized PLWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and 21% died during follow-up (compared with 23% and 20%, respectively). There was similar cumulative incidence of death over time by HIV status (P = .94). CONCLUSIONS: We found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared with a demographically similar patient group.

Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19.

BACKGROUND: Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. OBJECTIVE: To describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge. DESIGN: Retrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons. PARTICIPANTS: Hospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals. MAIN MEASURES: Readmission or return to ED following discharge. RESULTS: Of 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; Padjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. CONCLUSIONS: Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.