Massive rearrangements of cellular MicroRNA signatures are key drivers of hepatocyte dedifferentiation

Hepatocytes are dynamic cells that, upon injury, can alternate between nondividing differentiated and dedifferentiated proliferating states in vivo . However, in two‐dimensional cultures, primary human hepatocytes (PHHs) rapidly dedifferentiate, resulting in loss of hepatic functions that significantly limits their usefulness as an in vitro model of liver biology, liver diseases, as well as drug metabolism and toxicity. Thus, understanding the underlying mechanisms and stalling of the dedifferentiation process would be highly beneficial to establish more‐accurate and relevant long‐term in vitro hepatocyte models. Here, we present comprehensive analyses of whole proteome and transcriptome dynamics during the initiation of dedifferentiation during the first 24 hours of culture. We report that early major rearrangements of the noncoding transcriptome, hallmarked by increased expression of small nucleolar RNAs, long noncoding RNAs, microRNAs (miRNAs), and ribosomal genes, precede most changes in coding genes during dedifferentiation of PHHs, and we speculated that these modulations could drive the hepatic dedifferentiation process. To functionally test this hypothesis, we globally inhibited the miRNA machinery using two established chemically distinct compounds, acriflavine and poly‐l ‐lysine. These inhibition experiments resulted in a significantly impaired miRNA response and, most important, in a pronounced reduction in the down‐regulation of hepatic genes with importance for liver function. Thus, we provide strong evidence for the importance of noncoding RNAs, in particular, miRNAs, in hepatic dedifferentiation, which can aid the development of more‐efficient differentiation protocols for stem‐cell‐derived hepatocytes and broaden our understanding of the dynamic properties of hepatocytes with respect to liver regeneration. Conclusion: miRNAs are important drivers of hepatic dedifferentiation, and our results provide valuable information regarding the mechanisms behind liver regeneration and possibilities to inhibit dedifferentiation in vitro

Documentation of clinically relevant genomic biomarker allele frequencies in the next-generation FINDbase worldwide database

FINDbase (http://www.findbase.org) is a comprehensive data resource recording the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants underlying genetic disorders as well as pharmacogenomic biomarkers that can guide drug treatment. Here, we report significant new developments and technological advancements in the database architecture, leading to a completely revamped database structure, querying interface, accompanied with substantial extensions of data content and curation. In particular, the FINDbase upgrade further improves the user experience by introducing responsive features that support a wide variety of mobile and stationary devices, while enhancing computational runtime due to the use of a modern Javascript framework such as ReactJS. Data collection is significantly enriched, with the data records being divided in a Public and Private version, the latter being accessed on the basis of data contribution, according to the microattribution approach, while the front end was redesigned to support the new functionalities and querying tools. The abovementioned updates further enhance the impact of FINDbase, improve the overall user experience, facilitate further data sharing by microattribution, and strengthen the role of FINDbase as a key resource for personalized medicine applications and personalized public health

Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients

Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI)-algorithms, to be useful for COVID-19 infection via a proposed anti-cytokine effects and as an inhibitor of host cell viral propagation. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID-19 infection. We validated the AI-predicted biochemical inhibitory effects of baricitinib on human numb-associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids. These effects occurred at exposure levels seen clinically. In a case series of patients with bilateral COVID-19 pneumonia, baricitinib treatment was associated with clinical and radiologic recovery, a rapid decline in SARS-CoV-2 viral load, inflammatory markers, and IL-6 levels. Collectively, these data support further evaluation of the anti-cytokine and anti-viral activity of baricitinib and supports its assessment in randomized trials in hospitalized COVID-19 patients

Hypoxia-enhanced Blood-Brain Barrier Chip recapitulates human barrier function and shuttling of drugs and antibodies

The high selectivity of the human blood-brain barrier (BBB) restricts delivery of many pharmaceuticals and therapeutic antibodies to the central nervous system. Here, we describe an in vitro microfluidic organ-on-a-chip BBB model lined by induced pluripotent stem cell-derived human brain microvascular endothelium interfaced with primary human brain astrocytes and pericytes that recapitulates the high level of barrier function of the in vivo human BBB for at least one week in culture. The endothelium expresses high levels of tight junction proteins and functional efflux pumps, and it displays selective transcytosis of peptides and antibodies previously observed in vivo. Increased barrier functionality was accomplished using a developmentally-inspired induction protocol that includes a period of differentiation under hypoxic conditions. This enhanced BBB Chip may therefore represent a new in vitro tool for development and validation of delivery systems that transport drugs and therapeutic antibodies across the human BBB

Acute kidney injury in children

Acute kidney injury (AKI) (previously called acute renal failure) is characterized by a reversible increase in the blood concentration of creatinine and nitrogenous waste products and by the inability of the kidney to regulate fluid and electrolyte homeostasis appropriately. The incidence of AKI in children appears to be increasing, and the etiology of AKI over the past decades has shifted from primary renal disease to multifactorial causes, particularly in hospitalized children. Genetic factors may predispose some children to AKI. Renal injury can be divided into pre-renal failure, intrinsic renal disease including vascular insults, and obstructive uropathies. The pathophysiology of hypoxia/ischemia-induced AKI is not well understood, but significant progress in elucidating the cellular, biochemical and molecular events has been made over the past several years. The history, physical examination, and laboratory studies, including urinalysis and radiographic studies, can establish the likely cause(s) of AKI. Many interventions such as ‘renal-dose dopamine’ and diuretic therapy have been shown not to alter the course of AKI. The prognosis of AKI is highly dependent on the underlying etiology of the AKI. Children who have suffered AKI from any cause are at risk for late development of kidney disease several years after the initial insult. Therapeutic interventions in AKI have been largely disappointing, likely due to the complex nature of the pathophysiology of AKI, the fact that the serum creatinine concentration is an insensitive measure of kidney function, and because of co-morbid factors in treated patients. Improved understanding of the pathophysiology of AKI, early biomarkers of AKI, and better classification of AKI are needed for the development of successful therapeutic strategies for the treatment of AKI

Genome-wide association meta-analysis identifies 48 risk variants and highlights the role of the stria vascularis in hearing loss

Hearing loss is one of the top contributors to years lived with disability and is a risk factor for dementia. Molecular evidence on the cellular origins of hearing loss in humans is growing. Here, we performed a genome-wide association meta-analysis of clinically diagnosed and self-reported hearing impairment on 723,266 individuals and identified 48 significant loci, 10 of which are novel. A large proportion of associations comprised missense variants, half of which lie within known familial hearing loss loci. We used single-cell RNA-sequencing data from mouse cochlea and brain and mapped common-variant genomic results to spindle, root, and basal cells from the stria vascularis, a structure in the cochlea necessary for normal hearing. Our findings indicate the importance of the stria vascularis in the mechanism of hearing impairment, providing future paths for developing targets for therapeutic intervention in hearing loss

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe