Gene drives have enormous potential for the control of insect populations of medical and agricultural relevance. By preferentially biasing their own inheritance, gene drives can rapidly introduce genetic traits even if these confer a negative fitness effect on the population. We have recently developed gene drives based on CRISPR nuclease constructs that are designed to disrupt key genes essential for female fertility in the malaria mosquito. The construct copies itself and the associated genetic disruption from one homologous chromosome to another during gamete formation, a process called homing that ensures the majority of offspring inherit the drive. Such drives have the potential to cause long-lasting, sustainable population suppression, though they are also expected to impose a large selection pressure for resistance in the mosquito. One of these population suppression gene drives showed rapid invasion of a caged population over 4 generations, establishing proof of principle for this technology. In order to assess the potential for the emergence of resistance to the gene drive in this population we allowed it to run for 25 generations and monitored the frequency of the gene drive over time. Following the initial increase of the gene drive we observed a gradual decrease in its frequency that was accompanied by the spread of small, nuclease-induced mutations at the target gene that are resistant to further cleavage and restore its functionality. Such mutations showed rates of increase consistent with positive selection in the face of the gene drive. Our findings represent the first documented example of selection for resistance to a synthetic gene drive and lead to important design recommendations and considerations in order to mitigate for resistance in future gene drive applications. Gene drives are selfish genetic elements that are able to bias their own inheritance among offspring. Starting from very low frequencies they can rapidly invade a population in just a few generations, even when imposing a fitness cost. Gene drives based on the precise DNA cutting enzyme CRISPR have been shown recently to be highly efficient at copying themselves from one chromosome to the other during the process of gamete formation in mosquitoes, resulting in transmission to 99% of offspring instead of the 50% expected for a single gene copy. One proposed use for CRISPR-based gene drives is in the control of mosquitoes by designing the gene drive to target mosquito genes involved in fertility, thereby reducing their overall reproductive output and leading to population suppression. Like any intervention designed to suppress a population these gene drives are expected to select for mutations in the mosquito that are resistant to the drive and restore fertility to mosquitoes. We have analyzed the origin and selection of resistant alleles in caged populations of mosquitoes initiated with a gene drive construct targeting a female fertility gene. We find the selected alleles are in-frame insertions and deletions that are resistant to cleavage and restore female fertility. Our findings allow us to improve predictions on gene drive behaviour and to make concrete recommendations on how to improve future gene drive designs by decreasing the likelihood that they generate resistance.

We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80–0.95 and 0.83, 95% C.I. 0.74–0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.

Triple-negative breast cancer (TNBC) is a very aggressive and heterogeneous group of tumors. In order to develop effective therapeutic strategies, it is therefore essential to identify the subtype-specific molecular mechanisms underlying disease progression and resistance to chemotherapy. TNBC cells are highly dependent on exogenous cystine, provided by overexpression of the cystine/glutamate antiporter SLC7A11/xCT, to fuel glutathione synthesis and promote an oxidative stress response consistent with their high metabolic demands. Here we show that TNBC cells of the mesenchymal stem-like subtype (MSL) utilize forced cystine uptake to induce activation of the transcription factor NRF2 and promote a glutathione-independent mechanism to defend against oxidative stress. Mechanistically, we demonstrate that NRF2 activation is mediated by direct cysteinylation of the inhibitor KEAP1. Furthermore, we show that cystine-mediated NRF2 activation induces the expression of important genes involved in oxidative stress response, but also in epithelial-to-mesenchymal transition and stem-like phenotype. Remarkably, in survival analysis, four upregulated genes (OSGIN1, RGS17, SRXN1, AKR1B10) are negative prognostic markers for TNBC. Finally, expression of exogenous OSGIN1, similarly to expression of exogenous NRF2, can prevent cystine depletion-dependent death of MSL TNBC cells. The results suggest that the cystine/NRF2/OSGIN1 axis is a potential target for effective treatment of MSL TNBCs.

Metabolomics/lipidomics are important tools to identify novel biomarkers associated with liver damage. Patients with chronic liver disease (CLD) and hepatitis C virus (HCV) infection often have alterations in glucose, lipid and protein metabolism. The aim of this study was to evaluate if dysfunctional lipid and amino acid metabolism was associated with fibrosis severity and insulin resistance in CLD/HCV patients. We analyzed the baseline sera of 75 subjects with CLD/HCV infection HCV genotype-1, with proven liver biopsy prior to antiviral treatment. We measured amino acid (AA) and lipid concentration by gas and liquid chromatography-mass spectrometry respectively. Alterations in peripheral glucose metabolism due to insulin resistance (IR) were assesed by HOMA-IR (Glucose x Insulin/22.5), while adipose tissue IR was estimated as (Adipo-IR = Free Fatty Acids x Insulin). Baseline HOMA-IR and Adipo-IR were related to the degree of liver fibrosis. Reduction in ceramides 18:1/22:0, 18:1/24:0, diacylglycerol 42:6 and increased phosphocholine 40:6 were associated with higher fibrosis. Adipo-IR was related to lower levels of lysophosphatidylcholine 14:0 and 18:2 and with higher levels of sphingomyelin 18:2/24:0 and 18:2/24:1. Almost all AA were positively associated with Adipo-IR but not with HOMA-IR. We further confirmed the potential use of metabolomics and lipidomics in CLD/HCV subjects finding novel biomarkers of hepatic fibrosis and show that the adipose tissue IR is associated with more severe liver disease and is an important marker not only of altered lipid but also AA metabolism.

By examining the genotype calls generated by the 1000 Genomes Project we discovered that the human reference genome GRCh37 contains almost 20,000 loci in which the reference allele has never been observed in healthy individuals and around 70,000 loci in which it has been observed only in the heterozygous state. We show that a large fraction of this rare reference allele (RRA) loci belongs to coding, functional and regulatory elements of the genome and could be linked to rare Mendelian disorders as well as cancer. We also demonstrate that classical germline and somatic variant calling tools are not capable to recognize the rare allele when present in these loci. To overcome such limitations, we developed a novel tool, named RAREVATOR, that is able to identify and call the rare allele in these genomic positions. By using a small cancer dataset we compared our tool with two state-of-the-art callers and we found that RAREVATOR identified more than 1,500 germline and 22 somatic RRA variants missed by the two methods and which belong to significantly mutated pathways. These results show that, to date, the investigation of around 100,000 loci of the human genome has been missed by re-sequencing experiments based on the GRCh37 assembly and that our tool can fill the gap left by other methods. Moreover, the investigation of the latest version of the human reference genome, GRCh38, showed that although the GRC corrected almost all insertions and a small part of SNVs and deletions, a large number of functionally relevant RRAs still remain unchanged. For this reason, also future resequencing experiments, based on GRCh38, will benefit from RAREVATOR analysis results. RAREVATOR is freely available at http://sourceforge.net/projects/rarevator .