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Background Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. Results Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. Conclusions The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism .

Prostate cancer is one of the major health care problems, but the molecular pathogenesis has been relatively insufficiently elucidated. Recently, whole exome sequencing of prostate cancer identified recurrent mutations involving MED12 in Caucasian patients, which finding was not reproduced in one subsequent study by Sanger sequencing. Thus, we investigated mutation status of MED12 in exons 2 and 26 by Sanger sequencing in 102 radical prostatectomy cases from Korean patients. The analysis found the mutation in none of the cases. Therefore, MED12 mutation does not appear to represent a significant molecular alteration in this cohort of patients according to the analysis by the traditional ＂gold standard.＂

By incorporating major developments in genetics, ophthalmology, dermatology, and neuroimaging, to revise the diagnostic criteria for neurofibromatosis type 1 (NF1) and to establish diagnostic criteria for Legius syndrome (LGSS). We used a multistep process, beginning with a Delphi method involving global experts and subsequently involving non-NF experts, patients, and foundations/patient advocacy groups. We reached consensus on the minimal clinical and genetic criteria for diagnosing and differentiating NF1 and LGSS, which have phenotypic overlap in young patients with pigmentary findings. Criteria for the mosaic forms of these conditions are also recommended. The revised criteria for NF1 incorporate new clinical features and genetic testing, whereas the criteria for LGSS were created to differentiate the two conditions. It is likely that continued refinement of these new criteria will be necessary as investigators (1) study the diagnostic properties of the revised criteria, (2) reconsider criteria not included in this process, and (3) identify new clinical and other features of these conditions. For this reason, we propose an initiative to update periodically the diagnostic criteria for NF1 and LGSS. [Display omitted]

The speckle-type POZ protein （SPOP） is a tumor suppressor in prostate cancer （PCa）. SPOP somatic mutations have been reported in up to 15% of PCa of those of European descent. However, the genetic roles of SPOP in African American （AA）-PCa are currently unknown. We sequenced the SPOP gene to identify somatic mutations in 49 AA prostate tumors and identified three missense mutations （p.Y87C, p.F102S, and p.G111E） in five AA prostate tumors （10%） and one synonymous variant （p.11061） in one tumor. Intriguingly, all of mutations and variants clustered in exon six, and all of the mutations altered conserved amino acids. Moreover, two mutations （p.F102S and p.G111E） have only been identified in AA-PCa to date. Quantitative real-time polymerase chain reaction analysis showed a lower level of SPOP expression in tumors carrying SPOP mutations than their matched normal prostate tissues. In addition, SPOP mutations and novel variants were detected in 5 of 27 aggressive PCa and one of 22 less aggressive PCa （P 〈 0.05）. Further studies with increased sample size are needed to validate the clinicopathological significance of these SPOP mutations in AA-PCa.

The benign neoplasm cardiac myxoma represents one of the hallmarks of Carney complex (CNC), a familial multiple neoplasia syndrome. About 80% of the index cases have germline mutations in PRKAR1A encoding the RIα regulatory subunit of cAMP-dependent protein kinase A (PKA). However, the role of PRKAR1A gene mutations in the pathogenesis of non-CNC-associated sporadic cardiac myxoma is less well established. Here, we investigated the presence of PRKAR1A gene variants in a cohort of 24 sporadic cardiac myxomas using targeted next-generation sequencing. Our study shows that 14 out of 24 cases (58%) harbor PRKAR1A gene mutations, represented mostly by frameshift, nonsense, and splice site mutations (together 84%), leading to a premature stop codon predicted to be degraded via non-sense mediated mRNA decay. The other 16% of PRKAR1A genetic alterations involved missense mutations, often located in important functional domains of the regulatory subunit RIα. Notably, 64% ( n  = 9/14) of the cases harbored more than one PRKAR1A gene variant, suggesting compound heterozygous mutations either in cis or trans . In conclusion, PRKAR1A gene mutations associated with loss of RIα function leading to increased PKA activity were observed in ~ 60% of sporadic cardiac myxomas, strongly supporting an essential role for PKA in mediating formation of cardiac myxoma.

Background Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. Results Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. Conclusions End-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses.

Liquid biopsy analysis, mainly based on circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), provides an extremely powerful tool for the molecular profiling of cancer patients in real time. In this study, we directly compared PIK3CA hotspot mutations (E545K, H1047R) in EpCAM‐positive CTCs and paired plasma‐ctDNA in breast cancer (BrCa). PIK3CA hotspot mutations in CTCs and ctDNA were analyzed using our previously developed highly sensitive (0.05%), specific, and validated assay in plasma‐ctDNA from 77 early and 73 metastatic BrCa patients and 40 healthy donors. We further analyzed and directly compared PIK3CA hotspot mutations in DNAs isolated from CellSearch® cartridges (CTCs) and paired plasma‐ctDNA, in 56 cases of early and 27 cases of metastatic breast cancer, and 16 corresponding primary tumors. In plasma‐ctDNA, PIK3CA hotspot mutations were identified in 30/77(39.0%) early and 35/73(47.9%) metastatic BrCa cases; none (0/40, 0%) of the healthy donors’ plasma‐ctDNA samples were positive. Our direct comparison study in DNAs isolated from CellSearch® cartridges (CTCs) and paired plasma‐ctDNA from the same blood draws has shown a lack of concordance in early BrCa (27/56, 48.2%), while the concordance in the metastatic setting was higher (18/27, 66.6%). Our results were validated by ddPCR methodology, and the concordance between our assay and ddPCR for PIK3CA E545K hotspot mutation was 30/37 (81.1%). In many cases, PIK3CA hotspot mutations were detected in samples found to be negative for CTCs in CellSearch®. Our data demonstrated for the first time that (a) PIK3CA hotspot mutations are present at high frequencies in CTCs isolated from CellSearch® cartridges and paired plasma‐ctDNA both in early and metastatic BrCa, (b) the detection and concordance of PIK3CA hotspot mutations between plasma‐ctDNA and CTCs are higher in the metastatic setting, (c) PIK3CA mutational status significantly changes after therapeutic intervention, and (d) PIK3CA mutation detection in CTCs and plasma‐ctDNA provides complementary information. Our direct comparison study on the presence of PIK3CA hotspot mutations (E545K, H1047R) in EpCAM‐positive CTCs and paired plasma‐ctDNA demonstrated for the first time that PIK3CA mutations are present at high frequencies in both CTCs and paired plasma‐ctDNA and that there is a high concordance in the metastatic setting. PIK3CA mutational status significantly changes after therapeutic intervention.

To assess the quality of test suites, mutation analysis seeds artificial defects (mutations) into programs; a nondetected mutation indicates a weakness in the test suite. We present an automated approach to generate unit tests that detect these mutations for object-oriented classes. This has two advantages: First, the resulting test suite is optimized toward finding defects modeled by mutation operators rather than covering code. Second, the state change caused by mutations induces oracles that precisely detect the mutants. Evaluated on 10 open source libraries, our μtest prototype generates test suites that find significantly more seeded defects than the original manually written test suites.

Lumpy skin disease virus (LSDV) belongs to the genus Capripoxvirus and family Poxviridae . LSDV was endemic in most of Africa, the Middle East and Turkey, but since 2015, several outbreaks have been reported in other countries. In this study, we used whole genome sequencing approach to investigate the origin of the outbreak and understand the genomic landscape of the virus. Our study showed that the LSDV strain of 2022 outbreak exhibited many genetic variations compared to the Reference Neethling strain sequence and the previous field strains. A total of 1819 variations were found in 22 genome sequences, which includes 399 extragenic mutations, 153 insertion frameshift mutations, 234 deletion frameshift mutations, 271 Single nucleotide polymorphisms (SNPs) and 762 silent SNPs. Thirty-eight genes have more than 2 variations per gene, and these genes belong to viral-core proteins, viral binding proteins, replication, and RNA polymerase proteins. We highlight the importance of several SNPs in various genes, which may play an essential role in the pathogenesis of LSDV. Phylogenetic analysis performed on all whole genome sequences of LSDV showed two types of variants in India. One group of the variant with fewer mutations was found to lie closer to the LSDV 2019 strain from Ranchi while the other group clustered with previous Russian outbreaks from 2015. Our study highlights the importance of genomic characterization of viral outbreaks to not only monitor the frequency of mutations but also address its role in pathogenesis of LSDV as the outbreak continues.

Abstract Background: Palliative chemotherapy +/− targeted therapy in accordance with mutation profile is the norm in nonsmall-cell lung cancer (NSCLC). The objective of this audit was to determine the proportion of patients and physicians, who are unwilling to wait for the mutation report and the reasons thereof. Materials and Methods: All newly diagnosed NSCLC patients, post biopsy, seen at our center between November 2014 and January 2015 were included. The relationship between patient and physician decision and objective factors was explored by Fisher's exact test. The factors considered were Eastern Cooperative Oncology Group performance status (ECOG PS), the presence of a cough, hemoptysis, fatigue, and breathlessness. The agreement between patients and physician decision was tested by contingency table. Results: Out of 168 patients, 57 were unwilling to wait for driver mutation report (33.9% 95% confidence interval [CI] 27.2-41.4%). The most common reason provided by patients was symptomatic status (23, 40.1%). No other objective factor except PS ( P = 0.00) was associated with patient's decision. In 56 patients (33.4% 95% CI 26.6-40.7%), physicians were unwilling to wait. Among the tested factors ECOG PS ( P = 0.000), breathlessness ( P = 0.00) and fatigue ( P = 0.00) were associated with the decision of not waiting for the report. The percentage corrected value of contingency between patients and physician decision was 78.74%. Conclusion: At present, in our setup, nearly one-third of our NSCLC patients opt for immediate chemotherapy treatment and are unwilling to wait for mutation analysis report. The major reasons for such attitude is poor symptom control and deteriorating general condition.