Identification Of Key Mutations Research Articles

Exploring the key genomic variation in monkeypox virus during the 2022 outbreak

BackgroundIn 2022, a global outbreak of monkeypox occurred with a significant shift in its epidemiological characteristics. The monkeypox virus (MPXV) belongs to the B.1 lineage, and its genomic variations that were linked to the outbreak were investigated in this study. Previous studies have suggested that viral genomic variation plays a crucial role in the pathogenicity and transmissibility of viruses. Therefore, understanding the genomic variation of MPXV is crucial for controlling future outbreaks.MethodsThis study employed bioinformatics and phylogenetic approaches to evaluate the key genomic variation in the B.1 lineage of MPXV. A total of 979 MPXV strains were screened, and 212 representative strains were analyzed to identify specific substitutions in the viral genome. Reference sequences were constructed for each of the 10 lineages based on the most common nucleotide at each site. A total of 49 substitutions were identified, with 23 non-synonymous substitutions. Class I variants, which had significant effects on protein conformation likely to affect viral characteristics, were classified among the non-synonymous substitutions.ResultsThe phylogenetic analysis revealed 10 relatively monophyletic branches. The study identified 49 substitutions specific to the B.1 lineage, with 23 non-synonymous substitutions that were classified into Class I, II, and III variants. The Class I variants were likely responsible for the observed changes in the characteristics of circulating MPXV in 2022. These key mutations, particularly Class I variants, played a crucial role in the pathogenicity and transmissibility of MPXV.ConclusionThis study provides an understanding of the genomic variation of MPXV in the B.1 lineage linked to the recent outbreak of monkeypox. The identification of key mutations, particularly Class I variants, sheds light on the molecular mechanisms underlying the observed changes in the characteristics of circulating MPXV. Further studies can focus on functional domains affected by these mutations, enabling the development of effective control strategies against future monkeypox outbreaks.

Identification of key mutations in central nervous diffuse large B-cell lymphoma (DLBCL) by comprehensive analysis between sequencing and TCGA database.

BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin’s lymphoma, and hence, a comprehensive understanding based on the gene expression profile is imperative. Although several studies have identified some critical mutant genes of DLBCL, the disease in the central nervous system has not been investigated clearly. This study is aimed to identify some novel and important mutant genes of DLBCL in central nervous system.MethodsA total of 156 cases of central nervous tumors were collected from 2016 to 2018, in which the DLBCL cases were confirmed by H&E staining and immunohistochemistry. With the whole-genome high-throughput sequencing, the mutations of samples were identified. By matching with TCGA database, the common mutations of DLBCL were further confirmed.ResultsTwelve cases were designated as DLBCL, of which 1 case was classified into germinal center B cell (GCB) subtype, and 11 cases were non-GCB subtypes. The gene mutation spectrum demonstrated that the most common substitutions of six single bases were C>T/G>A, wherein the mutation frequency of C(C>T) G was the highest. The most common type of mutation is missense, and the most frequently mutated genes included MYD88, LRP1B, CD79B, GNA13 and PIM1. Based on the TCGA database, finally, the 4 significantly mutated genes (SMG), including MYD88, PIM1, CD79B, and BTG1 common in the above groups, were identified.ConclusionsTaken together, the analysis of the TCGA database and the results of the sequencing experiment displayed four mutations that might provide novel targets for the treatment of DLBCL.

Adaptive laboratory evolution of Corynebacterium glutamicum towards higher growth rates on glucose minimal medium

In this work, we performed a comparative adaptive laboratory evolution experiment of the important biotechnological platform strain Corynebacterium glutamicum ATCC 13032 and its prophage-free variant MB001 towards improved growth rates on glucose minimal medium. Both strains displayed a comparable adaptation behavior and no significant differences in genomic rearrangements and mutation frequencies. Remarkably, a significant fitness leap by about 20% was observed for both strains already after 100 generations. Isolated top clones (UBw and UBm) showed an about 26% increased growth rate on glucose minimal medium. Genome sequencing of evolved clones and populations resulted in the identification of key mutations in pyk (pyruvate kinase), fruK (1-phosphofructokinase) and corA encoding a Mg2+ importer. The reintegration of selected pyk and fruK mutations resulted in an increased glucose consumption rate and ptsG expression causative for the accelerated growth on glucose minimal medium, whereas corA mutations improved growth under Mg2+ limiting conditions. Overall, this study resulted in the identification of causative key mutations improving the growth of C. glutamicum on glucose. These identified mutational hot spots as well as the two evolved top strains, UBw and UBm, represent promising targets for future metabolic engineering approaches.

Abstract S1-05: In-depth genomic analysis of ER+ breast cancers during development of endocrine resistance

Abstract Background: Aromatase inhibitors (AIs) have an established role in the treatment of estrogen receptor alpha positive (ER+) post-menopausal breast cancer. Response rates are only 50-70% in the neoadjuvant setting and up to 40-50% of all adjuvantly treated patients will eventually relapse. Mutations in certain genes have been previously shown to confer resistance to therapy, and molecular subtype has associations with poor outcome (i.e. LumB and HER2E). In order to improve the outcomes of non-responders or patients who become resistant to endocrine treatment, the identification of key mutations, and their interaction with subtype, is crucial. Dynamic profiling of the same tumour demonstrating de novo or developing resistance after responding to one or more lines of endocrine treatment in the neoadjuvant setting provides a unique opportunity to identify such genomic changes. Methods: This series is unique in that it includes 17 post-menopausal women with ER+ breast cancer treated with neoadjuvant letrozole. 13 of these patients progressed on treatment or initially responded to treatment and then developed acquired resistance and 4 responded well. Dynamic clinical response was assessed for each patient using periodic 3D ultrasound measurements performed during treatment. Fresh tissue was taken before treatment and when the tumor was resistant to treatment (4 patients had 2 biopsies, 9 patients had 3 and 4 patients had 4 available biopsies taken). RNA and DNA were extracted from tumour and normal DNA obtained from either matched blood or normal lymphatic tissue. In total, 51 tumour samples were available and have completed RNA-Seq, with exome sequencing shortly to be completed. Results: From the RNA-seq data, the intrinsic subtype distribution was 9 LumA, 7 LumB, and 1 HER2-enriched; when stratified according to response, the "progressors" were 7 LumB, 5 LumA and 1 HER2-enriched, while "responders" were 3 LumA and 1 LumB. When examined in an unsupervised hierarchical clustering analysis along with >800 TCGA Breast tumor samples, 13/17 patients had all of their samples grouped immediately together, suggesting that the overall tumor phenotype was maintained. Interestingly, the most dominant change in gene expression was the observation that there were 5 "progressor" patients where the pre-treatment sample was LumB and all subsequent samples were LumA; we only observed one instance of a patient starting as LumA and changing to a LumB, who was also labelled as a "progressor". Full exome sequencing is underway and these results will be presented. Conclusion: Genomic analysis of progression suggests that an apparent "subtype shift" appears in a number of patients where a shift to LumA is seen; this apparent change may be reflective of decreased proliferation rates caused by therapy, or the acquisition of a true LumA phenotype. We cannot differentiate between these two hypotheses at this time, but expect that the exome sequencing will help to differentiate between these two hypotheses given the large number of mutations and copy number alterations that can differentiate between LumA vs. LumB. Citation Format: J Michael Dixon, Arran K Turnbull, Chris Fan, Joel S Parker, Xiaping He, Laura Arthur, Carlos Martinez-Perez, Lorna Renshaw, Charles Perou. In-depth genomic analysis of ER+ breast cancers during development of endocrine resistance [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S1-05.

How to run survival-endpoint phase II trials with 25% to 40% fewer patients.

2544 Background: New molecular information, whether arising from expression profiling, identification of key mutations, or proteomic analyses is making the personalization of therapy increasingly feasible. This leads to the fragmentation of common disease entities into multiple molecular subtypes. Having sufficient numbers of patients to demonstrate the effectiveness of novel targeted therapies for each subtype has therefore become challenging, essentially impeding progress. Methods: Use of sample size determination software allows for comparison of phase II trials with a target survival proportion at a given time T based upon Kaplan-Meier versus exponential survival methods. Results: The use of exponential survival fitting methods, in lieu of the currently implemented trial designs, to single arm phase II studies can routinely reduce patient numbers by 25-40% (Table) without compromising the statistical power. The biggest advantage is that survival information both before and after time T reduces the variability for the exponential fit estimate. This approach will lead to the saving of financial and patient resources for researchers. Moreover, the time saved will accelerate the approval of agents making the proposed trial designs ethically attractive. However, this approach is only applicable if the exponential distribution assumption behind the approach is valid. Conclusions: The exponential survival fitting method should replace the current standard approach in all instances unless the exponential distribution assumption is known to be false. Detailed examples and analysis will be presented. [Table: see text]