Establishment, characterization, and genetic profiling of patient-derived osteosarcoma cells from a patient with retinoblastoma.

Osteosarcoma is the most common malignant bone cancer in pediatric patients. Patients who respond poorly to chemotherapy experience worse clinical outcomes with a high mortality rate. The major challenge is the lack of effective drugs for these patients. To introduce new drugs for clinical approval, preclinical studies based on in vitro models must demonstrate the potency of the tested drugs, enabling the drugs to enter phase 1 clinical trials. Patient-derived cell culture is a promising testing platform for in vitro studies, as they more accurately recapitulate cancer states and genetic profiles compared to cell lines. In the present study, we established patient-derived osteosarcoma cells (PDC) from a patient who had previously been diagnosed with retinoblastoma. We identified a new variant of a germline mutation in the RB1 gene in the tissue of the patient. The biological effects of this PDC were studied to observe whether the cryopreserved PDC retained a feature of fresh PDC. The cryopreserved PDC preserved the key biological effects, including cell growth, invasive capability, migration, and mineralization, that define the conserved phenotypes compared to fresh PDC. From whole genome sequencing analysis of osteosarcoma tissue and patient-derived cells, we found that cryopreserved PDC was a minor population in the origin tissue and was selectively grown under the culture conditions. The cryopreserved PDC has a high resistance to conventional chemotherapy. This study demonstrated that the established cryopreserved PDC has the aggressive characteristics of osteosarcoma, in particular the chemoresistance phenotype that might be used for further investigation in the chemoresistant mechanism of osteosarcoma. In conclusion, the approach we applied for primary cell culture might be a promising method to generate in vitro models for functional testing of osteosarcoma.

Human Leukocyte Antigen Markers for Distinguishing Pustular Psoriasis and Adult-Onset Immunodeficiency with Pustular Reaction.

Pustular skin diseases, with pustular psoriasis (PP) being the prototype, are immune-mediated diseases characterized by the presence of multiple pustules, resulting from neutrophil accumulation in the layer of epidermis. Sterile skin pustular eruption, like PP, is also observed in 20-30% of patients with adult-onset immunodeficiency syndrome (AOID) and anti-interferon γ autoantibodies (IFN-γ), leading to challenges in classification and diagnosis. While the mechanism underlying this similar phenotype remains unknown, genetic factors in relation to the immune system are suspected of playing an important role. Here, the association between human leukocyte antigen (HLA) genes, which play essential roles in antigen presentation, contributing to immune response, and the presence of skin pustules in AOID and PP was revealed. HLA genotyping of 41 patients from multiple centers in Thailand who presented with multiple sterile skin pustules (17 AOID patients and 24 PP patients) was conducted using a next-generation-sequencing-based approach. In comparison to healthy controls, HLA-B*13:01 (OR = 3.825, 95%CI: 2.08-7.035), C*03:04 (OR = 3.665, 95%CI: 2.102-6.39), and DQB1*05:02 (OR = 2.134, 95%CI: 1.326-3.434) were significantly associated with the group of aforementioned conditions having sterile cutaneous pustules, suggesting a common genetic-related mechanism. We found that DPB1*05:01 (OR = 3.851, p = 0.008) and DRB1*15:02 (OR = 3.195, p = 0.033) have a significant association with pustular reaction in AOID patients, with PP patients used as a control. A variant in the DRB1 gene, rs17885482 (OR = 9.073, p = 0.005), was observed to be a risk factor for PP when using AOID patients who had pustular reactions as a control group. DPB1*05:01 and DRB1*15:02 alleles, as well as the rs17885482 variant in the DRB1 gene, were proposed as novel biomarkers to differentiate PP and AOID patients who first present with multiple sterile skin pustules without known documented underlying conditions.

Genetic diversity and ancestry of the Khmuic-speaking ethnic groups in Thailand: a genome-wide perspective.

The Khmuic-speaking populations are believed to be the descendants of one of the earliest groups to settle in Mainland Southeast Asia. In Thailand, there are two agricultural Khmuic-speaking ethnic groups, the Khamu and Lua (Htin). These peoples primarily reside in scattered locations along the mountainous Thailand-Laos border in Nan province. In this study, we conducted genome-wide SNP analysis on 81 individuals from three Khamu and two Lua villages in northern Thailand. Our findings revealed that both the Khamu and Lua groups possess genetic structures that are distinct from other ethnicities in Southeast Asia, indicating a unique history of migration and settlement. Within the Khmuic group, the Khamu populations living in different locations exhibited similar genetic structures and displayed genetic affinities only with some hill-tribes and Tai-Kadai (Kra-Dai)-speaking groups in Thailand, suggesting potential intermixing or cultural exchange. Furthermore, the Lua people displayed a distinctive population structure, which could be attributed to the founder effect and endogamous marriage practices. Additionally, we discovered a relationship between the Khmuic-speaking populations in Thailand and a Neolithic ancient sample obtained from the Tham Pha Ling archaeological site in Laos. This study provides new insight into genetic substructure within the Khmuic-speaking people and their potential relationship to the indigenous inhabitants of Mainland Southeast Asia.

Characterization of Cell-Free DNA Size Distribution in Osteosarcoma Patients.

PURPOSE: Cell-free DNA (cfDNA) analysis is a powerful tool for noninvasively predicting patient outcomes. We analyzed the size distribution of cfDNA and assessed its prognostic and diagnostic values in an osteosarcoma cohort. EXPERIMENTAL DESIGN: The fragment size distribution and level of cfDNA were analyzed in 15 healthy donors and 50 patients with osteosarcoma using automated capillary electrophoresis. The prognostic performance of cfDNA size analysis was assessed using univariate and multivariable analyses. By performing whole-genome sequencing of matched cfDNA and osteosarcoma tissue samples, we investigated the correlation between the size and mutation profiles of cfDNA and the mutation concordance between cfDNA and paired tissue tumors. RESULTS: The size of cfDNA fragments in patients with osteosarcoma was significantly shorter than in healthy donors, with the integrative analysis of size distribution and level of cfDNA achieving a high specificity and sensitivity of 100%. The short cfDNA fragment (150-bp cut-off) was an independent prognostic predictor in this osteosarcoma cohort [HR, 9.03; 95% confidence interval (CI), 1.13-72.20; P = 0.038]. Shortened cfDNA fragments were found to be a major source of mutations. Enrichment of cfDNA fragments with less than or equal to 150 bp by in silico size selection remarkedly improved the detection of copy-number variation signals up to 2.3-fold when compared with total cfDNA, with a higher concordance rate with matched osteosarcoma tissue. CONCLUSIONS: This finding demonstrated the potential of cfDNA size profiling in the stratification of poor prognostic patients with osteosarcoma. The short fragments of cfDNA are a promising source for boosting the detection of significant mutations in osteosarcoma. See related commentary by Weiser et al., p. 2017.

Potential target identification for osteosarcoma treatment: Gene expression re-analysis and drug repurposing.

Survival rate of osteosarcoma has remained plateaued for the past three decades. New treatment is needed to improve survival rate. Drug repurposing, a method to identify new indications of previous drugs, which saves time and cost compared to the de novo drug discovery. Data mining from gene expression profile was carried out and new potential targets were identified by using drug repurposing strategy. Selected data were newly categorized as pathophysiology and metastasis groups. Data were normalized and calculated the differential gene expression. Genes with log fold change ≥ 2 and adjusted p-value ≤ 0.05 were selected as primary candidate genes (PCGs). PCGs were further enriched to determine the secondary candidate genes (SCGs) by protein interaction analysis, upstream transcription factor and related-protein kinase identification. PCGs and SCGs were further matched with gene targeted of corresponding drugs from the Drug Repurposing Hub. A total of 778 targets were identified (360 from PCGs, and 418 from SCGs). This newly identified KLHL13 is a new candidate target based on its molecular function. KLHL13 was upregulated in clinical samples. We found 256 drugs from matching processes (50anti-cancerand206non-anticancerdrugs). Clinical trials of anti-cancer drugs from 5 targets (CDK4, BCL-2, JUN, SRC, PIK3CA) are being performed for osteosarcoma treatment. Niclosamide and synthetic PPARÉ£ ligands are candidates for repurposing due to the possibility based on their mechanism and pharmacology properties. Re-analysis of gene expression profile could identify new potential targets, confirm a current implication, and expand the chance of repurposing drugs for osteosarcoma treatment.

Co-evolutionary Signals Identify Burkholderia pseudomallei Survival Strategies in a Hostile Environment.

The soil bacterium Burkholderia pseudomallei is the causative agent of melioidosis and a significant cause of human morbidity and mortality in many tropical and subtropical countries. The species notoriously survives harsh environmental conditions but the genetic architecture for these adaptations remains unclear. Here we employed a powerful combination of genome-wide epistasis and co-selection studies (2,011 genomes), condition-wide transcriptome analyses (82 diverse conditions), and a gene knockout assay to uncover signals of "co-selection"-that is a combination of genetic markers that have been repeatedly selected together through B. pseudomallei evolution. These enabled us to identify 13,061 mutation pairs under co-selection in distinct genes and noncoding RNA. Genes under co-selection displayed marked expression correlation when B. pseudomallei was subjected to physical stress conditions, highlighting the conditions as one of the major evolutionary driving forces for this bacterium. We identified a putative adhesin (BPSL1661) as a hub of co-selection signals, experimentally confirmed a BPSL1661 role under nutrient deprivation, and explored the functional basis of co-selection gene network surrounding BPSL1661 in facilitating the bacterial survival under nutrient depletion. Our findings suggest that nutrient-limited conditions have been the common selection pressure acting on this species, and allelic variation of BPSL1661 may have promoted B. pseudomallei survival during harsh environmental conditions by facilitating bacterial adherence to different surfaces, cells, or living hosts.

Corrigendum: An Evolutionary Arms Race Between Burkholderia pseudomallei and Host Immune System: What Do We Know?

[This corrects the article DOI: 10.3389/fmicb.2020.612568.].

PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. The new model performed better than the sequence-based approaches with an F1 measure of 0.872, based on nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server is user-friendly, compatible with all devices, and available at bicep.kmutt.ac.th/photomod.

Photosynthetic protein classification using genome neighborhood-based machine learning feature.

Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genome neighborhood can provide additional useful information to identify photosynthetic proteins. We, therefore, expected that applying a computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their conserved neighboring genes observed by 'Phylo score', indicating their functions could be inferred from the genome neighborhood profile. Therefore, we created a new method for extracting patterns based on the genome neighborhood network (GNN) and applied them for the photosynthetic protein classification using ML algorithms. Random forest (RF) classifier using genome neighborhood-based features achieved the highest accuracy up to 87% in the classification of photosynthetic proteins and also showed better performance (Mathew's correlation coefficient = 0.718) than other available tools including the sequence similarity search (0.447) and ML-based method (0.361). Furthermore, we demonstrated the ability of our model to identify novel photosynthetic proteins compared to the other methods. Our classifier is available at http://bicep2.kmutt.ac.th/photomod_standalone, https://bit.ly/2S0I2Ox and DockerHub: https://hub.docker.com/r/asangphukieo/photomod.

An Evolutionary Arms Race Between Burkholderia pseudomallei and Host Immune System: What Do We Know?

A better understanding of co-evolution between pathogens and hosts holds promise for better prevention and control strategies. This review will explore the interactions between Burkholderia pseudomallei, an environmental and opportunistic pathogen, and the human host immune system. B. pseudomallei causes "Melioidosis," a rapidly fatal tropical infectious disease predicted to affect 165,000 cases annually worldwide, of which 89,000 are fatal. Genetic heterogeneities were reported in both B. pseudomallei and human host population, some of which may, at least in part, contribute to inter-individual differences in disease susceptibility. Here, we review (i) a multi-host-pathogen characteristic of the interaction; (ii) selection pressures acting on B. pseudomallei and human genomes with the former being driven by bacterial adaptation across ranges of ecological niches while the latter are driven by human encounter of broad ranges of pathogens; (iii) the mechanisms that generate genetic diversity in bacterial and host population particularly in sequences encoding proteins functioning in host-pathogen interaction; (iv) reported genetic and structural variations of proteins or molecules observed in B. pseudomallei-human host interactions and their implications in infection outcomes. Together, these predict bacterial and host evolutionary trajectory which continues to generate genetic diversity in bacterium and operates host immune selection at the molecular level.

Computational Design of Hypothetical New Peptides Based on a Cyclotide Scaffold as HIV gp120 Inhibitor.

Cyclotides are a family of triple disulfide cyclic peptides with exceptional resistance to thermal/chemical denaturation and enzymatic degradation. Several cyclotides have been shown to possess anti-HIV activity, including kalata B1 (KB1). However, the use of cyclotides as anti-HIV therapies remains limited due to the high toxicity in normal cells. Therefore, grafting anti-HIV epitopes onto a cyclotide might be a promising approach for reducing toxicity and simultaneously improving anti-HIV activity. Viral envelope glycoprotein gp120 is required for entry of HIV into CD4+ T cells. However, due to a high degree of variability and physical shielding, the design of drugs targeting gp120 remains challenging. We created a computational protocol in which molecular modeling techniques were combined with a genetic algorithm (GA) to automate the design of new cyclotides with improved binding to HIV gp120. We found that the group of modified cyclotides has better binding scores (23.1%) compared to the KB1. By using molecular dynamic (MD) simulation as a post filter for the final candidates, we identified two novel cyclotides, GA763 and GA190, which exhibited better interaction energies (36.6% and 22.8%, respectively) when binding to gp120 compared to KB1. This computational design represents an alternative tool for modifying peptides, including cyclotides and other stable peptides, as therapeutic agents before the synthesis process.