Abstract: Transcription factors (TFs) are DNA-binding proteins, which regulate many essential biological functions. In several cancer types, TF function is altered by various direct mechanisms, including gene amplification or deletion, point mutations, chromosomal translocations, expression alterations, as well as indirectly by non-coding DNA mutations influencing the binding of the TF. TFs are also actively involved in breast cancer (BC) initiation and progression. Herein, we have developed an open-access database, BC-TFdb (Breast Cancer Transcription Factors database), of curated, non-redundant TF involved in BC. The database provides BC driver TFs related information including genomic sequences, proteomic sequences, structural data, pathway information, mutations information, DNA binding residues, survival and therapeutic resources. The database will be a useful platform for researchers to obtain BC-related TF-specific information. High-quality datasets are downloadable for users to evaluate and develop computational methods for drug designing against BC. Database URL: https://www.dqweilab-sjtu.com/index.php.

Abstract: The infectious disease Coronavirus 2019 (COVID-19) continues to cause a global pandemic and, thus, the need for effective therapeutics remains urgent. Global research targeting COVID-19 treatments has produced numerous therapy-related data and established data repositories. However, these data are disseminated throughout the literature and web resources, which could lead to a reduction in the levels of their use. In this review, we introduce resource repositories for the development of COVID-19 therapeutics, from the genome and proteome to antiviral drugs, vaccines, and monoclonal antibodies. We briefly describe the data and usage, and how they advance research for therapies. Finally, we discuss the opportunities and challenges to preventing the pandemic from developing further.

Abstract: BACKGROUND: Drug-target interaction (DTI) plays a vital role in drug discovery. Identifying drug-target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug-target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. RESULTS: This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. CONCLUSIONS: Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.

Abstract: Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

Abstract: BACKGROUND: Gestational age is a useful proxy for assessing developmental maturity, but correct estimation of gestational age is difficult using clinical measures. DNA methylation at birth has proven to be an accurate predictor of gestational age. Previous predictors of epigenetic gestational age were based on DNA methylation data from the Illumina HumanMethylation 27 K or 450 K array, which have subsequently been replaced by the Illumina MethylationEPIC 850 K array (EPIC). Our aims here were to build an epigenetic gestational age clock specific for the EPIC array and to evaluate its precision and accuracy using the embryo transfer date of newborns from the largest EPIC-derived dataset to date on assisted reproductive technologies (ART). METHODS: We built an epigenetic gestational age clock using Lasso regression trained on 755 randomly selected non-ART newborns from the Norwegian Study of Assisted Reproductive Technologies (START)-a substudy of the Norwegian Mother, Father, and Child Cohort Study (MoBa). For the ART-conceived newborns, the START dataset had detailed information on the embryo transfer date and the specific ART procedure used for conception. The predicted gestational age was compared to clinically estimated gestational age in 200 non-ART and 838 ART newborns using MM-type robust regression. The performance of the clock was compared to previously published gestational age clocks in an independent replication sample of 148 newborns from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restrictions (PREDO) study-a prospective pregnancy cohort of Finnish women. RESULTS: Our new epigenetic gestational age clock showed higher precision and accuracy in predicting gestational age than previous gestational age clocks (R(2) = 0.724, median absolute deviation (MAD) = 3.14 days). Restricting the analysis to CpGs shared between 450 K and EPIC did not reduce the precision of the clock. Furthermore, validating the clock on ART newborns with known embryo transfer date confirmed that DNA methylation is an accurate predictor of gestational age (R(2) = 0.767, MAD = 3.7 days). CONCLUSIONS: We present the first EPIC-based predictor of gestational age and demonstrate its robustness and precision in ART and non-ART newborns. As more datasets are being generated on the EPIC platform, this clock will be valuable in studies using gestational age to assess neonatal development.

Abstract: There is ongoing interest in the alcohol industry to significantly reduce and/or add value to the liquid residue, vinasse, produced after the distillation and rectification of ethanol from sugar cane. Vinasse contains potassium, glycerol, and a protein component that can cause environmental issues if improperly disposed of. Currently, some industries have optimized their processes to reduce waste, and a significant proportion of vinasse is being considered for use as an additive in other industrial processes. In the manufacture of cement and asphalt, vinasse has been used in the mixtures at low concentrations, albeit with some physical and mechanical problems. This work is the first molecular approximation of the components of the sugar cane vinasse in an industrial context, and it provides atomic details of complex molecular events. In the current study, the major components of sugar cane vinasse, alone or complexed on the surface of calcium carbonate, were modeled and simulated using molecular dynamics. The results showed that the protein component, represented by the mannoprotein Mp1p, has a high affinity for forming hydrogen bonds with potassium and glycerol in the vinasse. Additionally, it provides atomic stability to the calcium carbonate surface, preserving the calcite crystalline structure in the same way potassium ions interact with the carbonate group through ion-dipole interactions to improve the cohesion of the modeled surface. On the contrary, when the glycerol molecule interacts with calcium carbonate using more than two hydrogen bonds, it triggers the breakdown of the crystalline structure of calcite expanding the ionic pair.

Abstract: Hemerocallis citrina Baroni (HC) is an edible plant in Asia, and it has been traditionally used for sleep-improvement. However, the bioactive components and mechanism of HC in sleep-improvement are still unclear. In this study, the sleep-improvement effect of HC hydroalcoholic extract was investigated based on a caffeine-induced insomnia model in Drosophila melanogaster (D. melanogaster), and the ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-ESI-Orbitrap-MS) and network pharmacology strategy were further combined to screen systematically the active constituents and mechanism of HC in sleep-improvement. The results suggested HC effectively regulated the number of nighttime activities and total sleep time of D. melanogaster in a dose-dependent manner and positively regulated the sleep bouts and sleep duration of D. melanogaster. The target screening suggested that quercetin, luteolin, kaempferol, caffeic acid, and nicotinic acid were the main bioactive components of HC in sleep-improvements. Moreover, the core targets (Akt1, Cat, Ple, and Sod) affected by HC were verified by the expression of the mRNA of D. melanogaster. In summary, this study showed that HC could effectively regulate the sleep of D. melanogaster and further clarifies the multi-component and multi-target features of HC in sleep-improvement, which provides a new insight for the research and utilization of HC.

Abstract: The COVID-19 main protease (Mpro), one of the conserved proteins of the novel coronavirus is crucial for its replication and so is a very lucrative drug target. Till now, there is no drug molecule that has been convincingly identified as the inhibitor of the function of this protein. The current pandemic situation demands a shortcut to quickly reach to a lead compound or a drug, which may not be the best but might serve as an interim solution at least. Following this notion, the present investigation uses virtual screening to find a molecule which is alraedy approved as a drug for some other disease but could be repurposed to inhibit Mpro. The potential of the present method of work to identify such a molecule, which otherwise would have been missed out, lies in the fact that instead of just using the crystallographically identified conformation of the receptor's ligand binding pocket, molecular dynamics generated ensemble of conformations has been used. It implicitly included the possibilities of "induced-fit" and/or "population shift" mechanisms of ligand fitting. As a result, the investigation has not only identified antiviral drugs like ribavirin, ritonavir, etc., but it has also captured a wide variety of drugs for various other diseases like amrubicin, cangrelor, desmopressin, diosmin, etc. as the potent possibilities. Some of these ligands are versatile to form stable interactions with various different conformations of the receptor and therefore have been statistically surfaced in the investigation. Overall the investigation offers a wide range of compounds for further testing to confirm their scopes of applications to combat the COVID-19 pandemic.

Abstract: Citrus root weevil (Diaprepes abbreviates) causes significant yield loss in citrus, especially in Florida. A promising source of control for this pest is biological control agents, namely, native entomopathogenic nematodes (EPNs) within the genus Steinernema. Two species of endemic EPN in Florida are S. diaparepesi, abundant within the central ridge, and S. khuongi, dominating the flatwood regions of the state. These citrus-growing regions differ significantly in their soil habitats, which impacts the potential success of biological control measures. Although the genome sequence of S. diaprepesi is currently available, the genome sequence of S. khuongi and identity of the symbiotic bacteria is still unknown. Understanding the genomic differences between these two nematodes and their favored habitats can inform successful biological control practices. Here, MiSeq libraries were used to simultaneously sequence and assemble the draft genome of S. khuongi and its associated symbionts. The final draft genome for S. khuongi has 8,794 contigs with a total length of approximately 82 Mb, a largest contig of 428,226 bp, and N50 of 46 kb; its BUSCO scores indicate that it is > 86% complete. An associated bacterial genome was assembled with a total length of approximately 3.5 Mb, a largest contig at 116,532 bp, and N50 of 17,487 bp. The bacterial genome encoded 3,721 genes, similar to other Xenorhabdus genomes. Comparative genomics identified the symbiotic bacteria of S. khuongi as Xenorhabdus poinarii. These new draft genomes of a host and symbiont can be used as a valuable tool for comparative genomics with other EPNs and its symbionts to understand host range and habitat suitability.

Abstract: BACKGROUND: Pharmacogenomics describes the link between gene variations (polymorphisms) and drug responses. In view of the implementation of precision medicine in personalized healthcare, pharmacogenetic tests have recently been introduced in the clinical practice. However, the translational aspects of such tests have been limited due to the lack of robust population-based evidence. MATERIALS: In this paper we present a novel pharmacogenetic panel (iDNA Genomics-PGx-CNS or PGx-CNS), consisting of 24 single nucleotide polymorphisms (SNPs) on 13 genes involved in the signaling or/and the metabolism of 28 approved drugs currently administered to treat diseases of the Central Nervous System (CNS). We have tested the PGx-CNS panel on 501 patient-derived DNA samples from a southeastern European population and applied biostatistical analyses on the pharmacogenetic associations involving drug selection, dosing and the risk of adverse drug events (ADEs). RESULTS: Results reveal the occurrences of each SNP in the sample and a strong correlation with the European population. Nonlinear principal component analysis strongly indicates co-occurrences of certain variants. The metabolization efficiency (poor, intermediate, extensive, ultra-rapid) and the frequency of clinical useful pharmacogenetic, associations in the population (drug relevance), are also described, along with four exemplar clinical cases illustrating the strong potential of the PGx-CNS panel, as a companion diagnostic assay. It is noted that pharmacogenetic associations involving copy number variations (CNVs) or the HLA gene were not included in this analysis. CONCLUSIONS: Overall, results illustrate that the PGx-CNS panel is a valuable tool supporting therapeutic medical decisions, urging its broad clinical implementation.