Genome-wide consequences of deleting any single gene.

Loss or duplication of chromosome segments can lead to further genomic changes associated with cancer. However, it is not known whether only a select subset of genes is responsible for driving further changes. To determine whether perturbation of any given gene in a genome suffices to drive subsequent genetic changes, we analyzed the yeast knockout collection for secondary mutations of functional consequence. Unlike wild-type, most gene knockout strains were found to have one additional mutant gene affecting nutrient responses and/or heat-stress-induced cell death. Moreover, independent knockouts of the same gene often evolved mutations in the same secondary gene. Genome sequencing identified acquired mutations in several human tumor suppressor homologs. Thus, mutation of any single gene may cause a genomic imbalance, with consequences sufficient to drive adaptive genetic changes. This complicates genetic analyses but is a logical consequence of losing a functional unit originally acquired under pressure during evolution.

Whi2 is a conserved negative regulator of TORC1 in response to low amino acids.

Yeast WHI2 was originally identified in a genetic screen for regulators of cell cycle arrest and later suggested to function in general stress responses. However, the function of Whi2 is unknown. Whi2 has predicted structure and sequence similarity to human KCTD family proteins, which have been implicated in several cancers and are causally associated with neurological disorders but are largely uncharacterized. The identification of conserved functions between these yeast and human proteins may provide insight into disease mechanisms. We report that yeast WHI2 is a new negative regulator of TORC1 required to suppress TORC1 activity and cell growth specifically in response to low amino acids. In contrast to current opinion, WHI2 is dispensable for TORC1 inhibition in low glucose. The only widely conserved mechanism that actively suppresses both yeast and mammalian TORC1 specifically in response to low amino acids is the conserved SEACIT/GATOR1 complex that inactivates the TORC1-activating RAG-like GTPases. Unexpectedly, Whi2 acts independently and simultaneously with these established GATOR1-like Npr2-Npr3-Iml1 and RAG-like Gtr1-Gtr2 complexes, and also acts independently of the PKA pathway. Instead, Whi2 inhibits TORC1 activity through its binding partners, protein phosphatases Psr1 and Psr2, which were previously thought to only regulate amino acid levels downstream of TORC1. Furthermore, the ability to suppress TORC1 is conserved in the SKP1/BTB/POZ domain-containing, Whi2-like human protein KCTD11 but not other KCTD family members tested.

EpiMOLAS: an intuitive web-based framework for genome-wide DNA methylation analysis.

BACKGROUND: DNA methylation is a crucial epigenomic mechanism in various biological processes. Using whole-genome bisulfite sequencing (WGBS) technology, methylated cytosine sites can be revealed at the single nucleotide level. However, the WGBS data analysis process is usually complicated and challenging. RESULTS: To alleviate the associated difficulties, we integrated the WGBS data processing steps and downstream analysis into a two-phase approach. First, we set up the required tools in Galaxy and developed workflows to calculate the methylation level from raw WGBS data and generate a methylation status summary, the mtable. This computation environment is wrapped into the Docker container image DocMethyl, which allows users to rapidly deploy an executable environment without tedious software installation and library dependency problems. Next, the mtable files were uploaded to the web server EpiMOLAS_web to link with the gene annotation databases that enable rapid data retrieval and analyses. CONCLUSION: To our knowledge, the EpiMOLAS framework, consisting of DocMethyl and EpiMOLAS_web, is the first approach to include containerization technology and a web-based system for WGBS data analysis from raw data processing to downstream analysis. EpiMOLAS will help users cope with their WGBS data and also conduct reproducible analyses of publicly available data, thereby gaining insights into the mechanisms underlying complex biological phenomenon. The Galaxy Docker image DocMethyl is available at https://hub.docker.com/r/lsbnb/docmethyl/. EpiMOLAS_web is publicly accessible at http://symbiosis.iis.sinica.edu.tw/epimolas/.

MIR144 and MIR451 regulate human erythropoiesis via RAB14.

Expression levels of MIR144 and MIR451 increase during erythropoiesis, a pattern that is conserved from zebrafish to humans. As these two miRs are expressed from the same polycistronic transcript, we manipulated MIR144 and MIR451 in human erythroid cells individually and together to investigate their effects on human erythropoiesis. Inhibition of endogenous human MIR451 resulted in decreased numbers of erythroid (CD71(hi) CD235a(hi) CD34(-) ) cells, consistent with prior studies in zebrafish and mice. In addition, inhibition of MIR144 impaired human erythroid differentiation, unlike in zebrafish and mouse studies where the functional effect of MIR144 on erythropoiesis was minimal. In this study, we found RAB14 is a direct target of both MIR144 and MIR451. As MIR144 and MIR451 expression increased during human erythropoiesis, RAB14 protein expression decreased. Enforced RAB14 expression phenocopied the effect of MIR144 and/or MIR451 depletion, whereas shRNA-mediated RAB14 knockdown protected cells from MIR144 and/or MIR451 depletion-mediated erythropoietic inhibition. RAB14 knockdown increased the frequency and number of erythroid cells, increased ß-haemoglobin expression, and decreased CBFA2T3 expression during human erythropoiesis. In summary, we utilized MIR144 and MIR451 to identify RAB14 as a novel physiological inhibitor of human erythropoiesis.

Deletion of tristetraprolin caused spontaneous reactive granulopoiesis by a non-cell-autonomous mechanism without disturbing long-term hematopoietic stem cell quiescence.

Tristetraprolin (TTP, Zfp36, Nup475, Tis11) dramatically reduces the stability of target mRNAs by binding to AU-rich elements in their 3' untranslated regions. Through this mechanism, TTP functions as a rheostatic, temporal regulator of gene expression. TTP knockout (KO) mice exhibit completely penetrant granulocytic hyperplasia. We have shown that the hematopoietic stem-progenitor cell compartment in TTP KO mice is also altered. Although no change was detected in long-term hematopoietic stem cell (HSC) frequency or function, as assayed by immunophenotypic markers or limiting dilution transplants, we observed increases in the frequencies and numbers of short-term HSCs, multipotent progenitors, and granulocyte-monocyte progenitors. This pattern is consistent with "reactive granulopoiesis," in which committed myeloid progenitors and more primitive progenitors cycle more actively to increase production of mature granulocytes in response to infection or adjuvant. We created reverse chimeras by transplanting wild-type bone marrow into TTP KO mice and found the "reactive granulopoiesis" phenocopied, indicating a non-hematopoietic stem-progenitor cell-autonomous mechanism. Correspondingly, we found elevated levels of the granulopoietic TTP targets IL-1ß, TNF-α, and IL-6 in the plasma of TTP KO mice. Consistent with the non-cell-autonomous nature of the phenotype, we found elevated levels of IL-1ß, TNF-α, and IL-6 transcripts in the livers of TTP KO mice and no detectable difference in the bone marrows. These findings demonstrate the importance of TTP in inflammatory homeostasis and highlight the ability of the hematopoietic system to respond to stress without significant numbers of quiescent HSCs entering the cell cycle.

Ultrasensitive detection of circulating LINE-1 ORF1p as a specific multi-cancer biomarker.

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. While proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1, L1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible detectable expression in corresponding normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore the potential of ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 M) ORF1p concentrations in patient plasma samples across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multi-analyte panel, and provides early therapeutic response monitoring in gastric and esophageal cancers. Together, these observations nominate ORF1p as a multi-cancer biomarker with potential utility for disease detection and monitoring.

Ultrasensitive Detection of Circulating LINE-1 ORF1p as a Specific Multicancer Biomarker.

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. Although proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible expression in normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 mol/L) ORF1p concentrations in plasma across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multianalyte panel, provides early therapeutic response monitoring in gastroesophageal cancers, and is prognostic for overall survival in gastroesophageal and colorectal cancers. Together, these observations nominate ORF1p as a multicancer biomarker with potential utility for disease detection and monitoring. SIGNIFICANCE: The LINE-1 ORF1p transposon protein is pervasively expressed in many cancers and is a highly specific biomarker of multiple common, lethal carcinomas and their high-risk precursors in tissue and blood. Ultrasensitive ORF1p assays from as little as 25 µL plasma are novel, rapid, cost-effective tools in cancer detection and monitoring. See related commentary by Doucet and Cristofari, p. 2502. This article is featured in Selected Articles from This Issue, p. 2489.

Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning.

Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates' anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.

AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation.

Motivation: Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods. Results: We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community. Availability and implementation: AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

A metagenomics study of hexabromocyclododecane degradation with a soil microbial community.

Hexabromocyclododecanes (HBCDs) are globally prevalent and persistent organic pollutants (POPs) listed by the Stockholm Convention in 2013. They have been detected in many environmental media from waterbodies to Plantae and even in the human body. Due to their highly bioaccumulative characterization, they pose an urgent public health issue. Here, we demonstrate that the indigenous microbial community in the agricultural soil in Taiwan could decompose HBCDs with no additional carbon source incentive. The degradation kinetics reached 0.173 day-1 after the first treatment and 0.104 day-1 after second exposure. With additional C-sources, the rate constants decreased to 0.054-0.097 day-1. The hydroxylic debromination metabolites and ring cleavage long-chain alkane metabolites were identified to support the potential metabolic pathways utilized by the soil microbial communities. The metagenome established by Nanopore sequencing showed significant compositional alteration in the soil microbial community after the HBCD treatment. After ranking, comparing relative abundances, and performing network analyses, several novel bacterial taxa were identified to contribute to HBCD biotransformation, including Herbaspirillum, Sphingomonas, Brevundimonas, Azospirillum, Caulobacter, and Microvirga, through halogenated / aromatic compound degradation, glutathione-S-transferase, and hydrolase activity. We present a compelling and applicable approach combining metagenomics research, degradation kinetics, and metabolomics strategies, which allowed us to decipher the natural attenuation and remediation mechanisms of HBCDs.

Comparative efficacy and mechanism of action of cardiac progenitor cells after cardiac injury.

Successful cell therapy requires cells to resist the hostile ischemic myocardium, be retained to continue secreting cardioprotective growth factors/exosomes, and resist immunological host responses. Clinically relevant stem/progenitor cells in a rodent model of acute myocardial infarction (MI) demonstrated that neonatal cardiac mesenchymal stromal cells (nMSCs) provide the most robust cardiac functional recovery. Transplanted nMSCs significantly increased the number of tissue reparative macrophages and regulatory T-cells and decreased monocyte-derived inflammatory macrophages and neutrophils in the host myocardium. mRNA microarray and single-cell analyses combined with targeted depletion studies established CD47 in nMSCs as a key molecule responsible for cell retention in the myocardium through an antiphagocytic mechanism regulated by miR34a-5p. Gain and loss-of-function studies demonstrated that miR34a-5p also regulated the production of exosomes and cardioprotective paracrine factors in the nMSC secretome. In conclusion, miR34a-5p and CD47 play an important role in determining the composition of nMSCs' secretome and immune evasion, respectively.

Investigating the Transcriptomic and Expression Presence-Absence Variation Exist in Japanese Eel (Anguilla japonica), a Primitive Teleost.

The pan-genome was defined as the complete gene set across strains, and it is built upon genes displaying presence-absence variations (PAVs); the pan-transcriptome is defined by recalling the pan-genome. Indeed, a PAV is reflected from the expression presence-absence variation (ePAV). In this study, treated with androgen, eels, which are a primitive fish from the basal lineage of Teleost, with different ovarian developments were chosen and submitted to RAN-sequencing. Transcriptomes were the assembly against eel genome scaffolds; a pair was the unit (the same eel before and after treatment) to analyze DEGs (differentially expressed genes); the core, unique, or accessory genes were identified, and the list of DEGs was analyzed to investigate ePAV. The results suggest that there was ePAV in Japanese eel, and the ePAV of eel was analyzed by pathway enrichment. These results signify the importance of genetic differential expression on the variations of phenotypes by androgen, and a transcriptomic approach appears to enable extracting multiple layers of genomic data.

Androgenic Sensitivities and Ovarian Gene Expression Profiles Prior to Treatment in Japanese Eel (Anguilla japonica).

Androgens stimulate ovarian development in eels. Our previous report indicated a correlation between the initial (debut) ovarian status (determined by kernel density estimation (KDE), presented as a probability density of oocyte size) and the consequence of 17MT treatment (change in ovary). The initial ovarian status appeared to be an important factor influencing ovarian androgenic sensitivity. We postulated that the sensitivities of initial ovaries are correlated with their gene expression profiles. Japanese eels underwent operation to sample the initial ovarian tissues, and the samples were stored in liquid nitrogen. Using high-throughput next-generation sequencing (NGS) technology, ovarian transcriptomic data were mined and analyzed based on functional gene classification with cutoff-based differentially expressed genes (DEGs); the ovarian status was transformed into gene expression profiles globally or was represented by a set of gene list. Our results also implied that the initial ovary might be an important factor influencing the outcomes of 17MT treatments, and the genes related with neuronal activities or neurogenesis seemed to play an essential role in the positive effect.

AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning.

Antimicrobial peptides (AMPs) are innate immune components that have recently stimulated considerable interest among drug developers due to their potential as antibiotic substitutes. AMPs are notable for their fundamental properties of microbial membrane structural interference and the biomedical applications of killing or suppressing microbes. New AMP candidates must be developed to oppose antibiotic resistance. However, the discovery of novel AMPs through wet-lab screening approaches is inefficient and expensive. The prediction model investigated in this study may help accelerate this process. We collected both the up-to-date AMP data set and unbiased negatives based on which the protein-encoding methods and deep learning model for AMPs were investigated. The external testing results indicated that our trained model achieved 90% precision, outperforming current methods. We implemented our model on a user-friendly web server, AI4AMP, to accurately predict the antimicrobial potential of a given protein sequence and perform proteome screening. IMPORTANCE Antimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitute for antibiotics. New candidates need to fight antibiotic resistance, while discovering novel AMPs through wet-lab screening approaches is inefficient and expensive. To accelerate the discovery of new AMPs, we both collected the up-to-date antimicrobial peptide data set and integrated the protein-encoding methods with a deep learning model. The trained model outperforms the current methods and is implemented into a user-friendly web server, AI4AMP, to accurately predict the antimicrobial properties of a given protein sequence and perform proteome screening. Author Video: An author video summary of this article is available.

Ultra-sensitive and rapid detection of nucleic acids and microorganisms in body fluids using single-molecule tethering.

Detection of microbial nucleic acids in body fluids has become the preferred method for rapid diagnosis of many infectious diseases. However, culture-based diagnostics that are time-consuming remain the gold standard approach in certain cases, such as sepsis. New culture-free methods are urgently needed. Here, we describe Single MOLecule Tethering or SMOLT, an amplification-free and purification-free molecular assay that can detect microorganisms in body fluids with high sensitivity without the need of culturing. The signal of SMOLT is generated by the displacement of micron-size beads tethered by DNA probes that are between 1 and 7 microns long. The molecular extension of thousands of DNA probes is determined with sub-micron precision using a robust and rapid optical approach. We demonstrate that SMOLT can detect nucleic acids directly in blood, urine and sputum at sub-femtomolar concentrations, and microorganisms in blood at 1 CFU mL-1 (colony forming unit per milliliter) threefold faster, with higher multiplexing capacity and with a more straight-forward protocol than amplified methodologies. SMOLT's clinical utility is further demonstrated by developing a multiplex assay for simultaneous detection of sepsis-causing Candida species directly in whole blood.

Mitochondrial death pathways in yeast and mammalian cells.

In mammals, mitochondria are important mediators of programmed cell death, and this process is often regulated by Bcl-2 family proteins. However, a role for mitochondria-mediated cell death in non-mammalian species is more controversial. New evidence from a variety of sources suggests that mammalian mitochondrial fission/division proteins also have the capacity to promote programmed cell death, which may involve interactions with Bcl-2 family proteins. Homologues of these fission factors and several additional mammalian cell death regulators are conserved in flies, worms and yeast, and have been suggested to regulate programmed cell death in these species as well. However, the molecular mechanisms by which these phylogenetically conserved proteins contribute to cell death are not known for any species. Some have taken the conserved pro-death activity of mitochondrial fission factors to mean that mitochondrial fission per se, or failed attempts to undergo fission, are directly involved in cell death. Other evidence suggests that the fission function and the cell death function of these factors are separable. Here we consider the evidence for these arguments and their implications regarding the origins of programmed cell death.

Mitochondrial programmed cell death pathways in yeast.

Whether or not yeast cell death is altruistic, apoptotic, or otherwise analogous to programmed cell death in mammals is controversial. However, growing attention to cell death mechanisms in yeast has produced several new papers that make a case for ancient origins of programmed death involving mitochondrial pathways conserved between yeast and mammals.

Neospora caninum expresses an unusual single-domain Kazal protease inhibitor that is discharged into the parasitophorous vacuole.

Serine protease inhibitors have been implicated in viral and parasite pathogenesis through their ability to inhibit apoptosis, provide protection against digestive enzymes in the gut and dictate host range specificity. Two Kazal family serine protease inhibitors from the obligate intracellular parasite Toxoplasma gondii (TgPI-1 and TgPI-2) have been characterised previously. Here, we describe the identification and initial characterisation of a novel Kazal inhibitor, NcPI-S, from a closely related apicomplexan parasite, Neospora caninum. Unlike the multidomain inhibitors identified in T. gondii, NcPI-S is a single domain inhibitor bearing a methionine in the position (P1) that typically dictates specificity for target proteases. Based on this, NcPI-S was predicted to inhibit elastase, chymotrypsin and subtilisin. However, we found that recombinant NcPI-S inhibited subtilisin very well, with little or no activity against elastase or chymotrypsin. NcPI-S localises to the dense granules and is secreted into the parasitophorous vacuole. Finally, antibodies raised against recombinant NcPI-S recognise two polypeptides in an N. caninum lysate, one with a molecular mass approximately 11 kDa and another at approximately 20 kDa. This, along with mass spectrometry analysis of recombinant NcPI-S, suggests that the inhibitor is expressed as a dimer in the parasite.

Regulation of RAB5C is important for the growth inhibitory effects of MiR-509 in human precursor-B acute lymphoblastic leukemia.

MicroRNAs (miRs) regulate essentially all cellular processes, but few miRs are known to inhibit growth of precursor-B acute lymphoblastic leukemias (B-ALLs). We identified miR-509 via a human genome-wide gain-of-function screen for miRs that inhibit growth of the NALM6 human B-ALL cell line. MiR-509-mediated inhibition of NALM6 growth was confirmed by 3 independent assays. Enforced miR-509 expression inhibited 2 of 2 additional B-ALL cell lines tested, but not 3 non-B-ALL leukemia cell lines. MiR-509-transduced NALM6 cells had reduced numbers of actively proliferating cells and increased numbers of cells undergoing apoptosis. Using miR target prediction algorithms and a filtering strategy, RAB5C was predicted as a potentially relevant target of miR-509. Enforced miR-509 expression in NALM6 cells reduced RAB5C mRNA and protein levels, and RAB5C was demonstrated to be a direct target of miR-509. Knockdown of RAB5C in NALM6 cells recapitulated the growth inhibitory effects of miR-509. Co-expression of the RAB5C open reading frame without its 3' untranslated region (3'UTR) blocked the growth-inhibitory effect mediated by miR-509. These findings establish RAB5C as a target of miR-509 and an important regulator of B-ALL cell growth with potential as a therapeutic target.