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

Pharmacogene sequencing of a Gabonese population with severe Plasmodium falciparum malaria reveals multiple novel variants with putative relevance for antimalarial treatment.

Malaria remains one of the most deadly diseases in Africa, particularly for children. While successful in reducing morbidity and mortality, antimalarial treatments are also a major cause of adverse drug reactions (ADRs). Host genetic variation in genes involved in drug disposition or toxicity constitutes an important determinant of ADR risk and can prime for parasite drug resistance. Importantly however, the genetic diversity in Africa is substantial and thus genetic profiles in one population cannot be reliably extrapolated to other ethnogeographic groups. Gabon is considered a high-transmission country with more than 460,000 malaria cases per year. Yet, the pharmacogenetic landscape of the Gabonese population or its neighboring countries has not been analyzed. Using targeted sequencing, we here profiled 21 pharmacogenes with importance for antimalarial treatment in 48 Gabonese pediatric patients with severe Plasmodium falciparum malaria. Overall, we identified 347 genetic variants of which 18 were novel and each individual was found to carry 87.3±9.2 SD variants across all analyzed genes. Importantly, 16.7% of these variants were population-specific, highlighting the need for high-resolution pharmacogenomic profiling. Between one in three and one in six individuals harbored reduced activity alleles of CYP2A6, CYP2B6, CYP2D6 and CYP2C8 with important implications for artemisinin, chloroquine and amodiaquine therapy. Furthermore, one in three patients harbored at least one G6PD deficient allele, suggesting considerably increased risk of hemolytic anemia upon exposure to aminoquinolines. Combined, our results reveal the unique genetic landscape of the Gabonese population and pinpoint the genetic basis for inter-individual differences in antimalarial drug response and toxicity

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

Abstract Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently hypothesized, using artificial intelligence (AI)-algorithms, to be useful for the treatment of COVID-19 infection via a proposed anti-cytokine effects and as an inhibitor of host cell viral propagation1,2. 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, which express hAAK1 and hGAK. 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. 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. This represents an important example of an AI-predicted treatment showing scientific and clinical promise during a global health crisis. Collectively, these data support further evaluation of the AI-derived hypothesis on anti-cytokine and anti-viral activity and supports its assessment in randomized trials in hospitalized COVID-19 patients.</jats:p

A multicenter assessment of single-cell models aligned to standard measures of cell health for prediction of acute hepatotoxicity.

Assessing the potential of a new drug to cause drug-induced liver injury (DILI) is a challenge for the pharmaceutical industry. We therefore determined whether cell models currently used in safety assessment (HepG2, HepaRG, Upcyte and primary human hepatocytes in conjunction with basic but commonly used endpoints) are actually able to distinguish between novel chemical entities (NCEs) with respect to their potential to cause DILI. A panel of thirteen compounds (nine DILI implicated and four non-DILI implicated in man) were selected for our study, which was conducted, for the first time, across multiple laboratories. None of the cell models could distinguish faithfully between DILI and non-DILI compounds. Only when nominal in vitro concentrations were adjusted for in vivo exposure levels were primary human hepatocytes (PHH) found to be the most accurate cell model, closely followed by HepG2. From a practical perspective, this study revealed significant inter-laboratory variation in the response of PHH, HepG2 and Upcyte cells, but not HepaRG cells. This variation was also observed to be compound dependent. Interestingly, differences between donors (hepatocytes), clones (HepG2) and the effect of cryopreservation (HepaRG and hepatocytes) were less important than differences between the cell models per se. In summary, these results demonstrate that basic cell health endpoints will not predict hepatotoxic risk in simple hepatic cells in the absence of pharmacokinetic data and that a multicenter assessment of more sophisticated signals of molecular initiating events is required to determine whether these cells can be incorporated in early safety assessment

JAK inhibition reduces SARS-CoV-2 liver infectivity and modulates inflammatory responses to reduce morbidity and mortality

Using AI, we identified baricitinib as having antiviral and anticytokine efficacy. We now show a 71% (95% CI 0.15 to 0.58) mortality benefit in 83 patients with moderate-severe SARS-CoV-2 pneumonia with few drug-induced adverse events, including a large elderly cohort (median age, 81 years). An additional 48 cases with mild-moderate pneumonia recovered uneventfully. Using organotypic 3D cultures of primary human liver cells, we demonstrate that interferon-α2 increases ACE2 expression and SARS-CoV-2 infectivity in parenchymal cells by greater than fivefold. RNA-seq reveals gene response signatures associated with platelet activation, fully inhibited by baricitinib. Using viral load quantifications and superresolution microscopy, we found that baricitinib exerts activity rapidly through the inhibition of host proteins (numb-associated kinases), uniquely among antivirals. This reveals mechanistic actions of a Janus kinase-1/2 inhibitor targeting viral entry, replication, and the cytokine storm and is associated with beneficial outcomes including in severely ill elderly patients, data that incentivize further randomized controlled trials

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