Europe PMC in 2023.

Europe PMC (https://europepmc.org/) is an open access database of life science journal articles and preprints, which contains over 42 million abstracts and over 9 million full text articles accessible via the website, APIs and bulk download. This publication outlines new developments to the Europe PMC platform since the last database update in 2020 (1) and focuses on five main areas. (i) Improving discoverability, reproducibility and trust in preprints by indexing new preprint content, enriching preprint metadata and identifying withdrawn and removed preprints. (ii) Enhancing support for text and data mining by expanding the types of annotations provided and developing the Europe PMC Annotations Corpus, which can be used to train machine learning models to increase their accuracy and precision. (iii) Developing the Article Status Monitor tool and email alerts, to notify users about new articles and updates to existing records. (iv) Positioning Europe PMC as an open scholarly infrastructure through increasing the portion of open source core software, improving sustainability and accessibility of the service.

A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data.

A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. We used BAGEA to integrate directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values < 10-7. For genes with estimated effect sizes in the top 25% quantile, up to 5% of the expression variance could be predicted. Based on our results, we recommend the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression biology.

FUS pathology in ALS is linked to alterations in multiple ALS-associated proteins and rescued by drugs stimulating autophagy.

Amyotrophic lateral sclerosis (ALS) is a lethal disease characterized by motor neuron degeneration and associated with aggregation of nuclear RNA-binding proteins (RBPs), including FUS. How FUS aggregation and neurodegeneration are prevented in healthy motor neurons remain critically unanswered questions. Here, we use a combination of ALS patient autopsy tissue and induced pluripotent stem cell-derived neurons to study the effects of FUS mutations on RBP homeostasis. We show that FUS' tendency to aggregate is normally buffered by interacting RBPs, but this buffering is lost when FUS mislocalizes to the cytoplasm due to ALS mutations. The presence of aggregation-prone FUS in the cytoplasm causes imbalances in RBP homeostasis that exacerbate neurodegeneration. However, enhancing autophagy using small molecules reduces cytoplasmic FUS, restores RBP homeostasis and rescues motor function in vivo. We conclude that disruption of RBP homeostasis plays a critical role in FUS-ALS and can be treated by stimulating autophagy.

TRADEOFFS IN ADAPTING BIOLOGICAL SYSTEMS.

Biological systems must sense and adapt to changes in their environment. Molecular networks capable of such adaptation belong to two well-known classes, feed-forward and feedback structures, but the fundamental limitations and tradeoffs of these two classes remain unknown. Here we study the advantages and limitations of the feedforward class using three-node circuits representative of these architectures. The feedforward model we investigate displays a tradeoff between the sensitivity of the response (its peak response) and its precision (its error in its return to steady-state). We suggest two ways in which this tradeoff can be alleviated: (1) by introducing a nonlinearity in the production of a specific node in the network, or (2) by adding a feedback loop to the input. We present analytical and numerical examples to support our findings.

Systematic comparative analysis of strand-specific RNA-seq library preparation methods for low input samples.

Despite the recent precipitous decline in the cost of genome sequencing, library preparation for RNA-seq is still laborious and expensive for applications such as high throughput screening. Limited availability of RNA generated by some experimental workflows poses an additional challenge and increases the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies-Swift RNA and Swift Rapid RNA, presently offered by Integrated DNA Technologies (IDT) -alongside the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark gene expression in these kits, at input quantities ranging between 10 to 500 ng. We found normalized read counts between all treatment groups to be in high agreement. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits offer shorter workflow times enabled by their patented Adaptase technology. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways directly attributable to input mRNA amount.

Biological evaluation of a natural steroid ester, Stigmasta-5(6), 22(23)-dien-3-beta-yl acetate isolated from the Himalayan herb Astilbe rivularis as potential antitumor agent.

BACKGROUND: Cancer remains a major world health issue due to its high morbidity and mortality rate. Plant based natural products (NPs) have played vital role in discovery of valuable anti-cancer drugs. Darjeeling Himalayan region has a rich diversity of therapeutic plants that can be utilized for development of novel drugs. AIM: We previously reported cytotoxic potential of rhizome extract of A.rivularis, a Darjeeling himalayan herb. Present study reports isolation and characterization of a phytosteroid from the plant rhizome in a bioassay-guided approach and evaluation of its anti-tumorigenic potential. RESULTS: The phytosteroid was characterized as stigmasta-5(6), 22(23)-dien-3-beta-yl acetate (A11) by various spectrometric techniques (IR, NMR, MS etc.). The catalytic inhibition and structural alteration of human dihydrofolate reductase (hDHFR) by A11 was evaluated using methotrexate (MTX), a DHFR inhibitor anticancer drug as a reference. A11 inhibited hDHFR activity with IC50 values of 1.20 µM A11 caused concentration dependent quenching of tryptophan fluorescence of hDHFR suggesting its effect on alteration of enzyme structure. Molecular docking of A11 on crystal structure of hDHFR revealed significant interaction with free energy of binding and Ki values of -10.86 kcal/mol and 11 nM, respectively. Subsequent in vitro studies at cellular level showed a relatively greater cytotoxic effect of A11 against human kidney (ACHN, IC50 60 µM) and liver (HepG2, IC5070 µM) cancer cells than their respective normal cells (HEK-293, IC50 350 µM and WRL-68, IC50 520 µM). Scanning electron microscopy of A11 treated cells revealed the morphological feature of apoptosis, like cell rounding and surface detachment, membrane blebbing, loss of cilia and increased number of pores of decreased sizes. A11 mediated apoptosis of cancer cells was found to be correlated with induction of intracellular of reactive oxygen species (ROS) level and fragmentation of genomic DNA.

Knocking out C9ORF72 Exacerbates Axonal Trafficking Defects Associated with Hexanucleotide Repeat Expansion and Reduces Levels of Heat Shock Proteins.

In amyotrophic lateral sclerosis (ALS) motor neurons (MNs) undergo dying-back, where the distal axon degenerates before the soma. The hexanucleotide repeat expansion (HRE) in C9ORF72 is the most common genetic cause of ALS, but the mechanism of pathogenesis is largely unknown with both gain- and loss-of-function mechanisms being proposed. To better understand C9ORF72-ALS pathogenesis, we generated isogenic induced pluripotent stem cells. MNs with HRE in C9ORF72 showed decreased axonal trafficking compared with gene corrected MNs. However, knocking out C9ORF72 did not recapitulate these changes in MNs from healthy controls, suggesting a gain-of-function mechanism. In contrast, knocking out C9ORF72 in MNs with HRE exacerbated axonal trafficking defects and increased apoptosis as well as decreased levels of HSP70 and HSP40, and inhibition of HSPs exacerbated ALS phenotypes in MNs with HRE. Therefore, we propose that the HRE in C9ORF72 induces ALS pathogenesis via a combination of gain- and loss-of-function mechanisms.

Viral Infections Exacerbate FUS-ALS Phenotypes in iPSC-Derived Spinal Neurons in a Virus Species-Specific Manner.

Amyotrophic lateral sclerosis (ALS) arises from an interplay of genetic mutations and environmental factors. ssRNA viruses are possible ALS risk factors, but testing their interaction with mutations such as in FUS, which encodes an RNA-binding protein, has been difficult due to the lack of a human disease model. Here, we use isogenic induced pluripotent stem cell (iPSC)-derived spinal neurons (SNs) to investigate the interaction between ssRNA viruses and mutant FUS. We find that rabies virus (RABV) spreads ALS phenotypes, including the formation of stress granules (SGs) with aberrant composition due to increased levels of FUS protein, as well as neurodegeneration and reduced restriction activity by FUS mutations. Consistent with this, iPSC-derived SNs harboring mutant FUS are more sensitive to human immunodeficiency virus (HIV-1) and Zika viruses (ZIKV). We demonstrate that RABV and HIV-1 exacerbate cytoplasmic mislocalization of FUS. Our results demonstrate that viral infections worsen ALS pathology in SNs with genetic risk factors, suggesting a novel role for viruses in modulating patient phenotypes.

Model-guided optogenetic study of PKA signaling in budding yeast.

In eukaryotes, protein kinase A (PKA) is a master regulator of cell proliferation and survival. The activity of PKA is subject to elaborate control and exhibits complex time dynamics. To probe the quantitative attributes of PKA dynamics in the yeast Saccharomyces cerevisiae, we developed an optogenetic strategy that uses a photoactivatable adenylate cyclase to achieve real-time regulation of cAMP and the PKA pathway. We capitalize on the precise and rapid control afforded by this optogenetic tool, together with quantitative computational modeling, to study the properties of feedback in the PKA signaling network and dissect the nonintuitive dynamic effects that ensue from perturbing its components. Our analyses reveal that negative feedback channeled through the Ras1/2 GTPase is delayed, pinpointing its time scale and its contribution to the dynamic features of the cAMP/PKA signaling network.

Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing.

A tradeoff between precision and throughput constrains all biological measurements, including sequencing-based technologies. Here, we develop a mathematical framework that defines this tradeoff between mRNA-sequencing depth and error in the extraction of biological information. We find that transcriptional programs can be reproducibly identified at 1% of conventional read depths. We demonstrate that this resilience to noise of "shallow" sequencing derives from a natural property, low dimensionality, which is a fundamental feature of gene expression data. Accordingly, our conclusions hold for ∼350 single-cell and bulk gene expression datasets across yeast, mouse, and human. In total, our approach provides quantitative guidelines for the choice of sequencing depth necessary to achieve a desired level of analytical resolution. We codify these guidelines in an open-source read depth calculator. This work demonstrates that the structure inherent in biological networks can be productively exploited to increase measurement throughput, an idea that is now common in many branches of science, such as image processing.