Incidental germline findings during molecular profiling of tumor tissues for precision oncology: molecular survey and methodological obstacles

Background A fraction of patients referred for complex molecular profiling of biopsied tumors may harbor germline variants in genes associated with the development of hereditary cancer syndromes (HCS). Neither the bioinformatic analysis nor the reporting of such incidental germline findings are standardized. Methods Data from Next-Generation Sequencing (NGS) of biopsied tumor samples referred for complex molecular profiling were analyzed for germline variants in HCS-associated genes. Analysis of variant origin was performed employing bioinformatic algorithms followed by manual curation. When possible, the origin of the variant was validated by Sanger sequencing of the sample of normal tissue. The variants’ pathogenicity was assessed according to ACMG/AMP. Results Tumors were sampled from 183 patients (Males: 75 [41.0%]; Females: 108 [59.0%]; mean [SD] age, 57.7 [13.3] years) and analysed by targeted NGS. The most common tumor types were colorectal (19%), pancreatic (13%), and lung cancer (10%). A total of 56 sequence variants in genes associated with HCS were detected in 40 patients. Of them, 17 variants found in 14 patients were predicted to be of germline origin, with 6 variants interpreted as pathogenic (PV) or likely pathogenic (LPV), and 9 as variants of uncertain significance (VUS). For the 41 out of 42 (97%) missense variants in HCS-associated genes, the results of computational prediction of variant origin were concordant with that of experimental examination. We estimate that Sanger sequencing of a sample of normal tissue would be required for ~ 1–7% of the total assessed cases with PV or LPV, when necessity to follow with genetic counselling referral in ~ 2–15% of total assessed cases (PV, LPV or VUS found in HCS genes). Conclusion Incidental findings of pathogenic germline variants are common in data from cancer patients referred for complex molecular profiling. We propose an algorithm for the management of patients with newly detected variants in genes associated with HCS.

Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Computational Prediction of Bacteriophage Host Ranges

Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.

Prediction of heptagonal bipyramidal nonacoordination in highly viable [OB-M©B7O7-BO]− (M = Fe, Ru, Os) complexes

Non-spherical distributions of ligand atoms in coordination complexes are generally unfavorable due to higher repulsion than for spherical distributions. To the best of our knowledge, non-spherical heptagonal bipyramidal nonacoordination is hitherto unreported, because of extremely high repulsion among seven equatorial ligand atoms. Herein, we report the computational prediction of such nonacoordination, which is constructed by the synergetic coordination of an equatorial hepta-dentate centripetal ligand (B7O7) and two axial mono-dentate ligands (-BO) in the gear-like mono-anionic complexes [OB-M©B7O7-BO]– (M = Fe, Ru, Os). The high repulsion among seven equatorial ligand B atoms has been compensated by the strong B–O bonding. These complexes are the dynamically stable (up to 1500 K) global energy minima with the HOMO-LUMO gaps of 7.15 to 7.42 eV and first vertical detachment energies of 6.14 to 6.66 eV (being very high for anions), suggesting their high probability for experimental realization in both gas-phase and condensed phases. The high stability stems geometrically from the surrounded outer-shell oxygen atoms and electronically from meeting the 18e rule as well as possessing the σ + π + δ triple aromaticity. Remarkably, the ligand-metal interactions are governed not by the familiar donation and backdonation interactions, but by the electrostatic interactions and electron-sharing bonding.

Synthesis of Nucleoside-like Molecules from a Pyrolysis Product of Cellulose and Their Computational Prediction as Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors

(1R,5S)-1-Hydroxy-3,6-dioxa-bicyclo[3.2.1]octan-2-one, available by an efficient catalytic pyrolysis of cellulose, has been applied as a chiral building block in the synthesis of seven new nucleoside analogues, with structural modifications on the nucleobase moiety and on the carboxyl- derived unit. The inverted configuration by Mitsunobu reaction used in their synthesis was verified by 2D-NOESY correlations, supported by the optimized structure employing the DFT methods. An in silico screening of these compounds as inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase has been carried out in comparison with both remdesivir, a mono-phosphoramidate prodrug recently approved for COVID-19 treatment, and its ribonucleoside metabolite GS-441524. Drug-likeness prediction and data by docking calculation indicated compound 6 [=(3S,5S)-methyl 5-(hydroxymethyl)-3-(6-(4-methylpiperazin-1-yl)-9H-purin-9-yl)tetrahydrofuran-3-carboxylate] as the best candidate. Furthermore, molecular dynamics simulation showed a stable interaction of structure 6 in RNA-dependent RNA polymerase (RdRp) complex and a lower average atomic fluctuation than GS-441524, suggesting a well accommodation in the RdRp binding pocket.

Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks

Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection. The combination of sequence and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the protein sequence through the selected properties. The shorter windows reflect the characteristics of the central residue, and the longer windows reflect the characteristics of the surroundings around the central residue. Moreover, to highlight the specificity of sequence and evolutionary properties, they are preprocessed, respectively. After that, the preprocessed properties are combined into feature matrices as the input of the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict IDPs effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz.

Characterization of Mutations Causing CYP21A2 Deficiency in Brazilian and Portuguese Populations

Deficiency of 21-hydroxylase enzyme (CYP21A2) represents 90% of cases in congenital adrenal hyperplasia (CAH), an autosomal recessive disease caused by defects in cortisol biosynthesis. Computational prediction and functional studies are often the only way to classify variants to understand the links to disease-causing effects. Here we investigated the pathogenicity of uncharacterized variants in the CYP21A2 gene reported in Brazilian and Portuguese populations. Physicochemical alterations, residue conservation, and effect on protein structure were accessed by computational analysis. The enzymatic performance was obtained by functional assay with the wild-type and mutant CYP21A2 proteins expressed in HEK293 cells. Computational analysis showed that p.W202R, p.E352V, and p.R484L have severely impaired the protein structure, while p.P35L, p.L199P, and p.P433L have moderate effects. The p.W202R, p.E352V, p.P433L, and p.R484L variants showed residual 21OH activity consistent with the simple virilizing phenotype. The p.P35L and p.L199P variants showed partial 21OH efficiency associated with the non-classical phenotype. Additionally, p.W202R, p.E352V, and p.R484L also modified the protein expression level. We have determined how the selected CYP21A2 gene mutations affect the 21OH activity through structural and activity alteration contributing to the future diagnosis and management of CYP21A2 deficiency.

Comprehensive Annotation and Functional Exploration of MicroRNAs in Lettuce

MicroRNA (miRNA) is an important endogenous post-transcriptional regulator, while lettuce (Lactuca sativa) is a leafy vegetable of global economic significance. However, there are few studies on miRNAs in lettuce, and research on miRNA regulatory network in lettuce is absent. In this study, through deep sequencing of small RNAs in different tissues, together with a reference genome, 157 high-confidence miRNA loci in lettuce were comprehensively identified, and their expression patterns were determined. Using a combination of computational prediction and high-throughput experimental verification, a set of reliable lettuce miRNA targets were obtained. Furthermore, through RNA-Seq, the expression profiles of these targets and a comprehensive view of the negative regulatory relationship between miRNAs and their targets was acquired based on a correlation analysis. To further understand miRNA functions, a miRNA regulatory network was constructed, with miRNAs at the core and combining transcription factors and miRNA target genes. This regulatory network, mainly composed of feed forward loop motifs, greatly increases understanding of the potential functions of miRNAs, and many unknown potential regulatory links were discovered. Finally, considering its specific expression pattern, Lsa-MIR408 as a hub gene was employed to illustrate the function of the regulatory network, and genetic experiments revealed its ability to increase the fresh weight and achene size of lettuce. In short, this work lays a solid foundation for the study of miRNA functions and regulatory networks in lettuce.