Complete blood count differences in a cohort of Down syndrome neonates with transient abnormal myelopoiesis screened for GATA1 pathogenic variants.

Transient abnormal myelopoiesis (TAM) raises the risk for acute myeloid leukemia of Down syndrome (DS) (ML-DS), and both are related to GATA1 pathogenic variants. Here, we analyzed which findings on complete blood count (CBC) are associated with TAM in a cohort of neonates with DS screened for GATA1 pathogenic variants. The CBCs were compared among 70 newborns with DS, including 16 patients (22.9%) with TAM (cases), and 54 patients (77.1%) without TAM (controls). TAM was defined as peripheral circulating blasts (PCBs) ≥ 1%. PCR and direct sequencing were used to screen DNA samples from peripheral blood for GATA1 exon 2 mutations. Multivariate logistic regression analyses determined that the mean count of lymphocytes was significantly higher in DS infants with TAM (p = .035) and that lymphocytosis confers a risk for TAM (adjusted odds ratio = 7.23, 95% confidence intervals: 2.02-25.92). Pathogenic variants of GATA1 were identified in 2 of 70 analyzed DS neonates (2.9%), of which one had ML-DS and another had an asymptomatic TAM. Among those DS infants with TAM, the GATA1 pathogenic variant detection was 12.5%. Our results indicated that lymphocytosis is associated with TAM in neonates with DS. However, since not all infants with an abnormal CBC had TAM, and not all infants with TAM had GATA1 pathogenic variants, we emphasize that only the search for GATA1 pathogenic variants allows the proper identification of the subgroup of DS infants with a real increasing in risk for ML-DS.

Acute promyelocytic leukemia with PML/RARA (bcr1, bcr2 and bcr3) transcripts in a pediatric patient.

Patients with acute promyelocytic leukemia (APL) exhibit the t(15;17)(q24.1;q21.2) translocation that produces the promyelocytic leukemia (PML)/retinoic acid receptor α (RARA) fusion gene. Different PML breakpoints yield three alternative molecular transcripts, bcr1, bcr2 and bcr3. The present study reports the simultaneous presence of three PML/RARA transcripts in a pediatric female patient diagnosed with APL, according to the clinical characteristics, immunophenotype and karyotype of the patient. The simultaneous presence of the PML/RARA transcripts were detected using reverse transcription-quantitative PCR (RT-qPCR). This was confirmed with HemaVision-28N Multiplex RT-qPCR, HemaVision-28Q qualitative RT-qPCR and the AmpliSeq RNA Myeloid Panel. To the best of our knowledge, the pediatric patient described in the present study is the first case found to exhibit all three PML/RARA transcripts (bcr1, bcr2 and bcr3). Additionally, a microarray analysis was performed to determine the expression profile, potential predictive biomarkers and the implications of this uncommon finding. According to the information obtained from molecular monitoring, the results reported in the present study were associated with a good patient prognosis. In addition, upregulated genes that are rare in acute myeloid leukemia were identified, and these genes may be promising diagnostic biomarkers for further study. For example, CCL-1 is present in leukemic stem cells, causing treatment failure and relapse, and α- and ß-defensins have been reported exclusively in chronic myeloid leukemia. However, the results of the present study confirmed that they may also be present in APL. Thus, these findings suggested a possible signaling pathway that involves the PML/RARA oncoprotein in APL.

An Innovative AI-based primer design tool for precise and accurate detection of SARS-CoV-2 variants of concern.

As the COVID-19 pandemic winds down, it leaves behind the serious concern that future, even more disruptive pandemics may eventually surface. One of the crucial steps in handling the SARS-CoV-2 pandemic was being able to detect the presence of the virus in an accurate and timely manner, to then develop policies counteracting the spread. Nevertheless, as the pandemic evolved, new variants with potentially dangerous mutations appeared. Faced by these developments, it becomes clear that there is a need for fast and reliable techniques to create highly specific molecular tests, able to uniquely identify VOCs. Using an automated pipeline built around evolutionary algorithms, we designed primer sets for SARS-CoV-2 (main lineage) and for VOC, B.1.1.7 (Alpha) and B.1.1.529 (Omicron). Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for the main lineage and each variant in a matter of hours. Preliminary in-silico validation showed that the sequences in the primer sets featured high accuracy. A pilot test in a laboratory setting confirmed the results: the developed primers were favorably compared against existing commercial versions for the main lineage, and the specific versions for the VOCs B.1.1.7 and B.1.1.529 were clinically tested successfully.

Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disorder characterized by disabling fatigue. Several studies have sought to identify diagnostic biomarkers, with varying results. Here, we innovate this process by combining both mRNA expression and DNA methylation data. We performed recursive ensemble feature selection (REFS) on publicly available mRNA expression data in peripheral blood mononuclear cells (PBMCs) of 93 ME/CFS patients and 25 healthy controls, and found a signature of 23 genes capable of distinguishing cases and controls. REFS highly outperformed other methods, with an AUC of 0.92. We validated the results on a different platform (AUC of 0.95) and in DNA methylation data obtained from four public studies on ME/CFS (99 patients and 50 controls), identifying 48 gene-associated CpGs that predicted disease status as well (AUC of 0.97). Finally, ten of the 23 genes could be interpreted in the context of the derailed immune system of ME/CFS.

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification.

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. The proposed approach is first tested on a tumor classification problem in order to separate 10 different types of cancer, with samples collected over 10 different clinical trials, and later is assessed on a cancer subtype classification task, with the aim to distinguish triple negative breast cancer from other subtypes of breast cancer. Overall, the presented methodology proves to be effective and compares favorably to other state-of-the-art feature selection methods.

In vitro effect of curcumin in combination with chemotherapy drugs in Ph+ acute lymphoblastic leukemia cells.

Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL), is characterized by the t(9;22)(q34q11) that generates the BCR-ABL protein with uncontrolled tyrosine kinase activity. Recently, a connection between BCR-ABL signaling with NF-κB activation mediated by CK2 has been hypothesized. Approximately 95% of patients with Ph+ ALL have the BCR-ABLp190 isoform, which causes aggressive leukemia with a high rate of chemotherapy resistance. Therefore, the use of compounds that could improve the efficacy of chemotherapy drugs is of particular interest. Curcumin is an active chemical in turmeric with antineoplastic potential; it regulates protein-kinases by modulating downstream molecular pathways. The present study evaluated the effect of curcumin in combination with the chemotherapeutic drugs vincristine, imatinib and daunorubicin in the human OP-1 cell line. Several doses of the chemotherapy drugs were examined, and the effects were evaluated following 12, 24 and 48 h of exposure. The interaction between the chemotherapy drugs and curcumin was determined by the dose-effect curve, which generated a combination index (CI); these data were represented in isobolograms. In addition, the individual effect of each drug was compared with its effect in combination with curcumin on cell viability, apoptosis degree, NF-κB activation and gene expression changes. The present study observed that curcumin potentiates the efficacy of vincristine and imatinib, generating an additive/synergistic effect in a dose- and time-dependent manner. These combinations significantly increased the apoptosis degree, decreased the activation of NF-κB and the expression of its regulated genes. Conversely treatment with daunorubicin + curcumin combination produced an antagonistic/additive effect in a dose-dependent manner, and this combination significantly increased the apoptosis degree. However, this effect seems not to be associated with NF-κB activity, as no significant changes were observed in its activation or in the expression of the genes that it regulates. The results of the present study demonstrate that curcumin may be used as an adjuvant agent for chemotherapy in patients with Ph+ ALL.