The Impact of Mutational Hotspots on Cancer Survival.

BACKGROUND: Cofactors, biomarkers, and the mutational status of genes such as TP53, EGFR, IDH1/2, or PIK3CA have been used for patient stratification. However, many genes exhibit recurrent mutational positions known as hotspots, specifically linked to varying degrees of survival outcomes. Nevertheless, few hotspots have been analyzed (e.g., TP53 and EGFR). Thus, many other genes and hotspots remain unexplored. METHODS: We systematically screened over 1400 hotspots across 33 TCGA cancer types. We compared the patients carrying a hotspot against (i) all cases, (ii) gene-mutated cases, (iii) other mutated hotspots, or (iv) specific hotspots. Due to the limited number of samples in hotspots and the inherent group imbalance, besides Cox models and the log-rank test, we employed VALORATE to estimate their association with survival precisely. RESULTS: We screened 1469 hotspots in 6451 comparisons, where 314 were associated with survival. Many are discussed and linked to the current literature. Our findings demonstrate associations between known hotspots and survival while also revealing more potential hotspots. To enhance accessibility and promote further investigation, all the Kaplan-Meier curves, the log-rank tests, Cox statistics, and VALORATE-estimated null distributions are accessible on our website. CONCLUSIONS: Our analysis revealed both known and putatively novel hotspots associated with survival, which can be used as biomarkers. Our web resource is a valuable tool for cancer research.

Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19.

OBJECTIVES: The data was collected for a cohort study to assess the capability of thermal videos in the detection of SARS-CoV-2. Using this data, a published study applied machine learning to analyze thermal image features for Covid-19 detection. DATA DESCRIPTION: The study recorded a set of measurements from 252 participants over 18 years of age requesting a SARS-CoV-2 PCR (polymerase chain reaction) test at the Hospital Zambrano-Hellion in Nuevo León, México. Data for PCR results, demographics, vital signs, food intake, activities and lifestyle factors, recently taken medications, respiratory and general symptoms, and a thermal video session where the volunteers performed a simple breath-hold in four different positions were collected. Vital signs recorded include axillary temperature, blood pressure, heart rate, and oxygen saturation. Each thermal video is split into 4 scenes, corresponding to front, back, left and right sides, and is available in MPEG-4 format to facilitate inclusion into pipelines for image processing. Raw JPEG images of the background between subjects are included to register variations in room temperatures.

Human Embryonic Stem Cell-Derived Immature Midbrain Dopaminergic Neurons Transplanted in Parkinsonian Monkeys.

Human embryonic stem cells (hESCs) differentiate into specialized cells, including midbrain dopaminergic neurons (DANs), and Non-human primates (NHPs) injected with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine develop some alterations observed in Parkinson's disease (PD) patients. Here, we obtained well-characterized DANs from hESCs and transplanted them into two parkinsonian monkeys to assess their behavioral and imaging changes. DANs from hESCs expressed dopaminergic markers, generated action potentials, and released dopamine (DA) in vitro. These neurons were transplanted bilaterally into the putamen of parkinsonian NHPs, and using magnetic resonance imaging techniques, we calculated the fractional anisotropy (FA) and mean diffusivity (MD), both employed for the first time for these purposes, to detect in vivo axonal and cellular density changes in the brain. Likewise, positron-emission tomography scans were performed to evaluate grafted DANs. Histological analyses identified grafted DANs, which were quantified stereologically. After grafting, animals showed signs of partially improved motor behavior in some of the HALLWAY motor tasks. Improvement in motor evaluations was inversely correlated with increases in bilateral FA. MD did not correlate with behavior but presented a negative correlation with FA. We also found higher 11C-DTBZ binding in positron-emission tomography scans associated with grafts. Higher DA levels measured by microdialysis after stimulation with a high-potassium solution or amphetamine were present in grafted animals after ten months, which has not been previously reported. Postmortem analysis of NHP brains showed that transplanted DANs survived in the putamen long-term, without developing tumors, in immunosuppressed animals. Although these results need to be confirmed with larger groups of NHPs, our molecular, behavioral, biochemical, and imaging findings support the integration and survival of human DANs in this pre-clinical PD model.

Heterozygous Knockout of ARID4B Using CRISPR/Cas9 Attenuates Some Aggressive Phenotypes in a Breast Cancer Cell Line.

Breast cancer is one of the leading causes of death in women around the world. Over time, many genes and mutations that are associated with the development of this disease have been identified. However, the specific role of many genes has not yet been fully elucidated. Higher ARID4B expression has been identified as a risk factor for diverse cancer types. Silencing experiments also showed that ARID4B is associated with developing cancer-associated characteristics. However, no transcriptomic studies have shown the overall cellular effect of loss of function in breast cancer in humans. This study addresses the impact of loss-of-function mutations in breast cancer MCF-7 cells. Using the CRISPR/Cas9 system, we generated mutations that caused heterozygous truncated proteins, isolating three monoclonal lines carrying insertions and deletions in ARID4B. We observed reduced proliferation and migration in in vitro experiments. In addition, from RNA-seq assays, a differential expression analysis shows known and novel deregulated cancer-associate pathways in mutated cells supporting the impact of ARID4B. For example, we found the AKT-PI3K pathway to be altered at the transcript level but through different genes than those reported for ARID4B. Our transcriptomic results also suggest new insights into the role of ARID4B in aggressiveness by the epithelial-to-mesenchymal transition and TGF-ß pathways and in metabolism through cholesterol and mevalonate pathways. We also performed exome sequencing to show that no off-target effects were apparent. In conclusion, the ARID4B gene is associated with some aggressive phenotypes in breast cancer cells.

MuTATE-an R package for comprehensive multi-objective molecular modeling.

MOTIVATION: Comprehensive multi-omics studies have driven advances in disease modeling for effective precision medicine but pose a challenge for existing machine-learning approaches, which have limited interpretability across clinical endpoints. Automated, comprehensive disease modeling requires a machine-learning approach that can simultaneously identify disease subgroups and their defining molecular biomarkers by explaining multiple clinical endpoints. Current tools are restricted to individual endpoints or limited variable types, necessitate advanced computation skills, and require resource-intensive manual expert interpretation. RESULTS: We developed Multi-Target Automated Tree Engine (MuTATE) for automated and comprehensive molecular modeling, which enables user-friendly multi-objective decision tree construction and visualization of relationships between molecular biomarkers and patient subgroups characterized by multiple clinical endpoints. MuTATE incorporates multiple targets throughout model construction and allows for target weights, enabling construction of interpretable decision trees that provide insights into disease heterogeneity and molecular signatures. MuTATE eliminates the need for manual synthesis of multiple non-explainable models, making it highly efficient and accessible for bioinformaticians and clinicians. The flexibility and versatility of MuTATE make it applicable to a wide range of complex diseases, including cancer, where it can improve therapeutic decisions by providing comprehensive molecular insights for precision medicine. MuTATE has the potential to transform biomarker discovery and subtype identification, leading to more effective and personalized treatment strategies in precision medicine, and advancing our understanding of disease mechanisms at the molecular level. AVAILABILITY AND IMPLEMENTATION: MuTATE is freely available at GitHub (https://github.com/SarahAyton/MuTATE) under the GPLv3 license.

The retinoic acid response is a minor component of the cardiac phenotype in H9c2 myoblast differentiation.

The H9c2 myoblast cell line, isolated from the left ventricular tissue of rat, is currently used in vitro as a mimetic for skeletal and cardiac muscle due to its biochemical, morphological, and electrical/hormonal signaling properties. During culture, H9c2 cells acquire a myotube phenotype, where a critical component is the inclusion of retinoic acid (RA). The results from some authors on H9c2 suggested that thousands of genes respond to RA stimuli, while others report hundreds of genes responding to RA over different cell types. In this article, using a more appropriate experimental design, we first confirm the H9c2 cardiac phenotype with and without RA and report transcriptomic and physiological changes regarding calcium handling, bioenergetics, and other biological concepts. Interestingly, of the 2360 genes showing a transcriptional change, 622 genes were statistically associated with the RA response. Of these genes, only 305 were RA-specific, and the rest also showed a culture-time component. Thus, the major expression changes (from 74 to 87%) were indeed due to culture conditions over time. Unexpectedly, only a few components of the retinol pathway in KEGG responded to RA. Our results show the role of RA in the H9c2 cultures impacting the interpretation using H9c2 as an in vitro model.

The Enigmatic Aliphatic Acetogenins and Their Correlations With Lipids During Seed Germination and Leaf Development of Avocado (Persea americana Mill.).

Lipids in avocados have been widely studied due to their nutritional value and several reported bioactivities. Aliphatic acetogenins are a relevant component of the avocado lipidome and have been tested for several potential food and pharma industries applications. This work followed the evolution of avocado fatty acids (FAs) and aliphatic acetogenins during seed germination and leaf growth. Oil extracts of embryonic axes, cotyledons, and leaves from seedlings and trees were divided to analyze free acetylated acetogenins (AcO-acetogenins), and free FAs. Embryonic axes from germinating seeds contained the highest amount of AcO-acetogenins and FAs; this tissue also accumulated the most diverse FA profile with up to 22 detected moieties. Leaves presented the highest variations in AcO-acetogenin profiles during development, although leaves from seedlings accumulated the simplest FA profile with only 10 different FAs. Remarkably, AcO-acetogenins represented half of the carbons allocated to lipids in grown leaves, while embryonic axes and cotyledons always contained more carbons within FAs during germination. Thus, we hypothesized the use of the AcO-acetogenin acyl chain for energy production toward ß-oxidation. Also, α-linolenic and docosahexaenoic acids (DHAs) were proposed as close AcO-acetogenin intermediaries based on a correlation network generated using all these data. Another part of the oil extract was fractionated into different lipid classes before transesterification to profile FAs and acetogenins bound to lipids. Acetogenin backbones were identified for the first time in triglycerides from cotyledons and mainly in polar lipids (which include phospholipids) in all developing avocado tissues analyzed. Seed tissues accumulated preferentially polar lipids during germination, while triglycerides were consumed in cotyledons. Seedling leaves contained minute amounts of triglycerides, and polar lipids increased as they developed. Results from this work suggest acetogenins might be part of the energy and signaling metabolisms, and possibly of membrane structures, underlining the yet to establish role(s) of these unusual lipids in the avocado plant physiology.

COVID-19 classification using thermal images.

SIGNIFICANCE: There is a scarcity of published research on the potential role of thermal imaging in the remote detection of respiratory issues due to coronavirus disease-19 (COVID-19). This is a comprehensive study that explores the potential of this imaging technology resulting from its convenient aspects that make it highly accessible: it is contactless, noninvasive, and devoid of harmful radiation effects, and it does not require a complicated installation process. AIM: We aim to investigate the role of thermal imaging, specifically thermal video, for the identification of SARS-CoV-2-infected people using infrared technology and to explore the role of breathing patterns in different parts of the thorax for the identification of possible COVID-19 infection. APPROACH: We used signal moment, signal texture, and shape moment features extracted from five different body regions of interest (whole upper body, chest, face, back, and side) of images obtained from thermal video clips in which optical flow and super-resolution were used. These features were classified into positive and negative COVID-19 using machine learning strategies. RESULTS: COVID-19 detection for male models [receiver operating characteristic (ROC) area under the ROC curve (AUC) = 0.605 95% confidence intervals (CI) 0.58 to 0.64] is more reliable than for female models (ROC AUC = 0.577 95% CI 0.55 to 0.61). Overall, thermal imaging is not very sensitive nor specific in detecting COVID-19; the metrics were below 60% except for the chest view from males. CONCLUSIONS: We conclude that, although it may be possible to remotely identify some individuals affected by COVID-19, at this time, the diagnostic performance of current methods for body thermal imaging is not good enough to be used as a mass screening tool.

Characterizing genes associated with cancer using the CRISPR/Cas9 system: A systematic review of genes and methodological approaches.

The CRISPR/Cas9 system enables a versatile set of genomes editing and genetic-based disease modeling tools due to its high specificity, efficiency, and accessible design and implementation. In cancer, the CRISPR/Cas9 system has been used to characterize genes and explore different mechanisms implicated in tumorigenesis. Different experimental strategies have been proposed in recent years, showing dependency on various intrinsic factors such as cancer type, gene function, mutation type, and technical approaches such as cell line, Cas9 expression, and transfection options. However, the successful methodological approaches, genes, and other experimental factors have not been analyzed. We, therefore, initially considered more than 1,300 research articles related to CRISPR/Cas9 in cancer to finally examine more than 400 full-text research publications. We summarize findings regarding target genes, RNA guide designs, cloning, Cas9 delivery systems, cell enrichment, and experimental validations. This analysis provides valuable information and guidance for future cancer gene validation experiments.

Germline Variants in Cancer Genes from Young Breast Cancer Mexican Patients.

Breast cancer (BC) is one of the most frequent cancer types in women worldwide. About 7% is diagnosed in young women (YBC) less than 40 years old. In Mexico, however, YBC reaches 15% suggesting a higher genetic susceptibility. There have been some reports of germline variants in YBC across the world. However, there is only one report from a Mexican population, which is not restricted by age and limited to a panel of 143 genes resulting in 15% of patients carrying putatively pathogenic variants. Nevertheless, expanding the analysis to whole exome involves using more complex tools to determine which genes and variants could be pathogenic. We used germline whole exome sequencing combined with the PeCanPie tool to analyze exome variants in 115 YBC patients. Our results showed that we were able to identify 49 high likely pathogenic variants involving 40 genes on 34% of patients. We noted many genes already reported in BC and YBC worldwide, such as BRCA1, BRCA2, ATM, CHEK2, PALB2, and POLQ, but also others not commonly reported in YBC in Latin America, such as CLTCL1, DDX3X, ERCC6, FANCE, and NFKBIE. We show further supporting and controversial evidence for some of these genes. We conclude that exome sequencing combined with robust annotation tools and further analysis, can identify more genes and more patients affected by germline mutations in cancer.

A systematic review and functional bioinformatics analysis of genes associated with Crohn's disease identify more than 120 related genes.

BACKGROUND: Crohn's disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn's disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking. METHODS: We conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn's disease. RESULTS: We identified 126 genes associated with Crohn's disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn's disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn's disease is genetically underdiagnosed. CONCLUSIONS: We identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn's disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease .

Multivariate genome-wide association study models to improve prediction of Crohn's disease risk and identification of potential novel variants.

BACKGROUND: Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) that affects the gastrointestinal tract with diverse symptoms. At present, genome-wide association studies (GWAS) has discovered more than 140 genetic loci associated with CD from several datasets. Using the usual univariate GWAS methods, researchers have discovered common variants with small effects. Univariate methods assume independence among the variants that miss subtle combinatorial signals. Multivariate approaches have improved risk prediction and have complemented univariate methods for elucidating the etiology of complex traits and potential novel associations. However, the current multivariate models for CD have been assessed for three datasets (published from 2006 to 2008) under unrelated methodological settings showing a broad performance spectrum. Notably, these multivariate studies do not analyze potential novel variants. Here, we aimed to perform a robust multivariate analysis of a CD dataset different from the one commonly used, and we used the information yielded by the models to identify whether the generated models could provide additional information about the potential novel variants of CD. METHODS: Therefore, we compared different multivariate methods and models, LASSO (least absolute shrinkage and selection operator), XGBoost, random forest (RF), Bootstrap stage-wise model selection (BSWiMS), and LDpred, using a strict random subsampling approach to predict the CD risk using a recent GWAS dataset, United Kingdom IBD IBD Genetics Consortium (UKIBDGC), made available in 2017, that had not been used for CD prediction studies. In addition, we assessed the effect of common strategies by increasing and decreasing the number of single-nucleotide polymorphism (SNP) markers (using genotype imputation and linkage disequilibrium (LD)-clumping). RESULTS: We found that the LDpred model without any imputation was the best model among all the tested models for predicting the CD risk (area under the receiver operating characteristic curve (AUROC) = 0.667 ± 0.024) in this dataset. We validated the best models using a second dataset (National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) IBD Genetics Consortium, which was previously used in CD prediction studies) in which LDpred was also the best method with a similar performance (AUROC = 0.634 ± 0.009). Based on the importance of the variants yielded by the multivariate models, we identified an unnoticed region within chromosome 6, tagged by SNP rs4945943; this region was close to the gene MARCKS, which appeared to contribute to CD risk. CONCLUSIONS: This research is the first multivariate prediction analysis applied to the UKIBDGC dataset. Our robust multivariate setting analysis enabled us to identify a potential variant that contributed to the CD risk. Multivariate methods are valuable tools for identifying genes that contribute to disease risk.

Generating human papillomavirus (HPV) reference databases to maximize genomic mapping.

Genomic experiments analyzing human papillomaviruses (HPVs) require a carefully selected list of sequences as a reference database to map millions of reads. The available sources, such as the Papillomavirus Episteme (PaVE), are organized based on variations in the L1 gene rather than the whole HPV sequence. Moreover, the PaVE process uses complex multiple sequence alignments containing hundreds or thousands of sequences. These issues complicate the generation of a reference database for genomics, leading to the generation of per-analysis-defined databases. Here, we propose a de novo strategy considering all HPV sequences reported in the NCBI database to define a subset of highly representative HPV sequences. The strategy is based on oligonucleotide frequency profiling of the whole sequence followed by hierarchical clustering. Using data from HPV capture experiments, we demonstrate that this strategy selects suitable sequences as a reference database to map most mappable reads unambiguously. We provide some recommendations to improve HPV mapping. The generated .fasta files can be accessed at https://github.com/vtrevino/HPV-Ref-Genomes .

Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis.

PURPOSE: Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients. METHODS: We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality. RESULTS: A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (߈ = 0.66; P = .048) on survival (߈ = 0.37; P = .008). CONCLUSION: Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.

Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling.

BACKGROUND: Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans. OBJECTIVE: This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk. METHODS: Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class. RESULTS: Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters. CONCLUSION: We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.

Dynamic landscape of chromatin accessibility and transcriptomic changes during differentiation of human embryonic stem cells into dopaminergic neurons.

Chromatin architecture influences transcription by modulating the physical access of regulatory factors to DNA, playing fundamental roles in cell identity. Studies on dopaminergic differentiation have identified coding genes, but the relationship with non-coding genes or chromatin accessibility remains elusive. Using RNA-Seq and ATAC-Seq we profiled differentially expressed transcripts and open chromatin regions during early dopaminergic neuron differentiation. Hierarchical clustering of differentially expressed genes, resulted in 6 groups with unique characteristics. Surprisingly, the abundance of long non-coding RNAs (lncRNAs) was high in the most downregulated transcripts, and depicted positive correlations with target mRNAs. We observed that open chromatin regions decrease upon differentiation. Enrichment analyses of accessibility depict an association between open chromatin regions and specific functional pathways and gene-sets. A bioinformatic search for motifs allowed us to identify transcription factors and structural nuclear proteins that potentially regulate dopaminergic differentiation. Interestingly, we also found changes in protein and mRNA abundance of the CCCTC-binding factor, CTCF, which participates in genome organization and gene expression. Furthermore, assays demonstrated co-localization of CTCF with Polycomb-repressed chromatin marked by H3K27me3 in pluripotent cells, progressively decreasing in neural precursor cells and differentiated neurons. Our work provides a unique resource of transcription factors and regulatory elements, potentially involved in the acquisition of human dopaminergic neuron cell identity.

Chemosensitivity analysis and study of gene resistance on tumors and cancer stem cell isolates from patients with colorectal cancer.

Colorectal cancer (CRC) is one of the main causes of mortality. Recent studies suggest that cancer stem cells (CSCs) can survive after chemotherapy and promote tumor invasiveness and aggression. According to a higher hierarchy complexity of CSC, different protocols for isolation, expansion, and characterization have been used; however, there are no available resistance biomarkers that allow predicting the clinical response of treatment 5­fluorouracil (5FU) and oxaliplatin. Therefore, the primary aim of the present study was to analyze the expression of gene resistance on tumors and CSC­derived isolates from patients CRC. In the present study, adenocarcinomas of the colon and rectum (CRAC) were classified based on an in vitro adenosine triphosphate­based chemotherapy response assay, as sensitive and resistant and the percentage of CD24 and CD44 markers are evaluated by immunohistochemistry. To isolate resistant colon­CSC, adenocarcinoma tissues resistant to 5FU and oxaliplatin were evaluated. Finally, all samples were sequenced using a custom assay with chemoresistance­associated genes to find a candidate gene on resistance colon­CSC. Results showed that 59% of the CRC tissue analyzed was resistant and had a higher percentage of CD44 and CD24 markers. An association was found in the expression of some genes between the tumor­resistant tissue and CSC. Overall, isolates of the CSC population CD44+ resistant to 5FU and oxaliplatin demonstrated different expression profiles; however, the present study was able to identify overexpression of the KRT­18 gene, in most of the isolates. In conclusion, the results of the present study showed overexpression of KRT­18 in CD44+ cells is associated with chemoresistance to 5FU and oxaliplatin in CRAC.

Analysis of HPV Integrations in Mexican Pre-Tumoral Cervical Lesions Reveal Centromere-Enriched Breakpoints and Abundant Unspecific HPV Regions.

Human papillomavirus (HPV) DNA integration is a crucial event in cervical carcinogenesis. However, scarce studies have focused on studying HPV integration (HPVint) in early-stage cervical lesions. Using HPV capture followed by sequencing, we investigated HPVint in pre-tumor cervical lesions. Employing a novel pipeline, we analyzed reads containing direct evidence of the integration breakpoint. We observed multiple HPV infections in most of the samples (92%) with a median integration rate of 0.06% relative to HPV mapped reads corresponding to two or more sequence breakages. Unlike cancer studies, most integrations events were unique (supported by one read), consistent with the lack of clonal selection. Congruent to other studies, we found that breakpoints could occur, practically, in any part of the viral genome. We noted that L1 had a higher frequency of rupture integration (25%). Based on host genome integration frequencies, we found previously reported integration sites in cancer for genes like FHIT, CSMD1, and LRP1B and putatively many new ones such as those exemplified in CSMD3, ROBO2, and SETD3. Similar host integrations regions and genes were observed in diverse HPV types within many genes and even equivalent integration positions in different samples and HPV types. Interestingly, we noted an enrichment of integrations in most centromeres, suggesting a possible mechanism where HPV exploits this structural machinery to facilitate integration. Supported by previous findings, overall, our analysis provides novel information and insights about HPVint.

Identification of a Novel Pathogenic Rearrangement Variant of the APC Gene Associated with a Variable Spectrum of Familial Cancer.

Familial adenomatous polyposis (FAP) is an autosomal-dominant condition characterized by the presence of multiple colorectal adenomas, caused by germline variants in the adenomatous polyposis coli (APC) gene. More than 300 germline variants have been characterized. The detection of novel variants is important to understand the mechanisms of pathophysiology. We identified a novel pathogenic germline variant using next-generation sequencing (NGS) in a proband patient. The variant is a complex rearrangement (c.422+1123_532-577 del ins 423-1933_423-1687 inv) that generates a complete deletion of exon 5 of the APC gene. To study the variant in other family members, we designed an endpoint PCR method followed by Sanger sequencing. The variant was identified in the proband patient's mother, one daughter, her brother, two cousins, a niece, and a second nephew. In patients where the variant was identified, we found atypical clinical symptoms, including mandibular, ovarian, breast, pancreatic, and gastric cancer. Genetic counseling and cancer prevention strategies were provided for the family. According to the American College of Medical Genetics (ACMG) guidelines, this novel variant is considered a PVS1 variant (very strong evidence of pathogenicity), and it can be useful in association with clinical data for early surveillance and suitable treatment.

Plasma and vacuolar membrane sphingolipidomes: composition and insights on the role of main molecular species.

Lipid structures affect membrane biophysical properties such as thickness, stability, permeability, curvature, fluidity, asymmetry, and interdigitation, contributing to membrane function. Sphingolipids are abundant in plant endomembranes and plasma membranes (PMs) and comprise four classes: ceramides, hydroxyceramides, glucosylceramides, and glycosylinositolphosphoceramides (GIPCs). They constitute an array of chemical structures whose distribution in plant membranes is unknown. With the aim of describing the hydrophobic portion of sphingolipids, 18 preparations from microsomal (MIC), vacuolar (VM), PM, and detergent-resistant membranes (DRM) were isolated from Arabidopsis (Arabidopsis thaliana) leaves. Sphingolipid species, encompassing pairing of long-chain bases and fatty acids, were identified and quantified in these membranes. Sphingolipid concentrations were compared using univariate and multivariate analysis to assess sphingolipid diversity, abundance, and predominance across membranes. The four sphingolipid classes were present at different levels in each membrane: VM was enriched in glucosylceramides, hydroxyceramides, and GIPCs; PM in GIPCs, in agreement with their key role in signal recognition and sensing; and DRM in GIPCs, as reported by their function in nanodomain formation. While a total of 84 sphingolipid species was identified in MIC, VM, PM, and DRM, only 34 were selectively distributed in the four membrane types. Conversely, every membrane contained a different number of predominant species (11 in VM, 6 in PM, and 17 in DRM). This study reveals that MIC, VM, PM, and DRM contain the same set of sphingolipid species but every membrane source contains its own specific assortment based on the proportion of sphingolipid classes and on the predominance of individual species.