Modified RNA probes reacted with thiol‐containing molecules as well as with cysteine‐ or histidine‐containing peptides to form stable covalent products. They also reacted selectively with RNA‐binding proteins to form cross‐linked conjugates in high conversions thanks to proximity effect. Our modified nucleotide and RNA probes are promising tools for applications in RNA (bio)conjugations or RNA proteomics.Reactive RNA probes are useful for studying and identifying RNA‐binding proteins. To that end, we designed and synthesized chloroacetamide‐linked 7‐deaza‐ATP which was a good substrate for T7 RNA polymerase in  A modified nucleotide for construction of reactive RNA probes that form covalent cross‐linked conjugates with Cys‐ or His‐containing peptides and proteins is reported. The nucleotide and probes are promising tools for applications in RNA (bio)conjugations or RNA proteomics. RNA‐protein interactions (RPIs) are of paramount importance in cell biology and are subject of intensive current research.Some of these drawbacks could be overcome with RNA probes bearing amino acid‐specific reactive groups. In DNA, several nucleotides bearing reactive modifications have been described that can be enzymatically incorporated to DNA probes, e.g. vinylsulfonamide reacting with cysteines (Cys),CATPrA bearing CA group at position 7 of 7‐deazaadenine base tethered through aminopropagyl linker. Synthesis of CATPrA was performed by single step Sonogashira cross‐coupling reaction of 7‐iodo‐7‐deazaadenosine‐5′‐O‐triphosphate (ITPrA) with N‐(propargyl)‐chloroacetamide (1) in analogy to previous work.2, CuI, TPPTS and DIPEA in a mixture of H2O/acetonitrile (2 : 1) . Next, we designed three DNA templates with different sequences encoding for 35‐nucleotide (nt) RNA containing one (CA35RNA_1A), three (CA35RNA_3A) or seven (CA35RNA_7A) CA modifications . To avoid usage of hazardous radioactive labelling, we tagged the short RNA with Cy5 fluorophore through ligation reactionCA‐Cy521RNA_1A in virtually full conversion as determined by dPAGE analysis . The product was also characterized by MS‐MALDI‐TOF analysis .To test reactivity of the modified RNA probes, we performed model bioconjugation reactions with Cys‐ and His‐containing peptides and thiol‐linked biotin and fluorescein. To this end, we designed and prepared a short 20‐nt CA‐biotin‐Cy521RNA_1A (21 %), CA‐FL‐Cy521RNA_1A (24 %), which were characterized by gel mobility shift assay ‐C was the most reactive giving the conjugated product CA‐pept‐(+)‐C‐Cy521RNA_1A in good 36 % yield with 100 equiv of the peptide. Remarkably, when the amount of pept‐(+)‐C was further increased, a lower‐mobile side‐product was observed on dPAGE ‐H was then less reactive giving CA‐pept‐(+)‐H‐Cy521RNA_1A in 17 %. Also using large excess of GSH tripeptide gave the desired conjugate CA‐GSH‐Cy521RNA_1A (10 %). All these bioconjugation products were fully characterized by dPAGE ‐C also by LC‐ESI‐MS . In case of negatively charged peptide with Cys and/or peptides not containing any Cys or His, reaction with modified RNA produced only traces of bioconjugation products CA‐pept‐(−)‐C‐Cy521RNA_1A, CA‐pept‐K‐Cy521RNA_1A, CA‐pept‐R‐Cy521RNA_1A as determined by dPAGE analysis  or in HEPES‐NaOH buffer at neutral pH in presence of 10 000 equiv excess of thiol‐linked (bio)molecules. Reactions with biotin‐ or fluorescein‐thiol proceeded with moderate conversions to give desired conjugation products s Figure , we usedCA‐Cy521RNA_1A and above‐mentioned proteins (Section 2.11 in Supporting Information). Contrary to studied model bioconjugation reactions, the cross‐linking reactions were performed with only 2 equiv of each of the proteins since we expected strong contribution of proximity effect. Cross‐linking was monitored by denaturing SDS‐PAGE that revealed lower mobility bands corresponding to formed covalent RNA‐protein adducts CA‐HuR‐Cy521RNA_1A (30 %), CA‐HIV‐RT‐Cy521RNA_1A (28 %) and CA‐hAgo2‐Cy521RNA_1A (22 %) ‐modified RNA with proteins. RNA‐protein cross‐linking was studied with three different RBPs with diverse biological functions, each of them containing several Cys and His amino acids (Section 4 in Supporting Information). We selected human argonaute 2 protein (hAgo2),‐HuR‐Cy5 0 %, 21RNCA‐Cy536RNA_1A), three (CA‐Cy536RNA_3A) or seven (CA‐Cy536RNA_7A) CA modifications  and compared them in cross‐linking reactions with HuR . BSA was used as off‐target control. Cross‐linking conversions increased with number of reactive groups, however for 7 modifications, the selectivity was already compromised . Hence, to balance specificity and conversions, density of modifications needs to be considered on case‐by‐case basis.To uncover relationship between number of modifications, cross‐linking efficiency and selectivity for RBPs, we prepared RNA probes with one .Cross‐linking was furthermore corroborated by western blot analysis . Shift of the proteins was observed only in case of modified CA20RNA_1Aprotein‐ conjugates were confirmed in case of HIV‐RT and HuR protein also by intact ESI‐MS analysis . Locations of cross‐linked amino acids in HIV‐RT, HuR and/or hAgo2 protein were determined by nano‐LC–MS/MS analysis of protein‐nucleic acids digests . For hAgo2 protein, the representative example of RBP, we compared positions of cross‐linked residues with available crystal structure to infer any general relationship between covalent capture and position of the residue within RBP. Five, out of total 22 Cys present in hAgo2 protein cross‐linked. All captured residues are in neighborhood of RNA . However, since other Cys in close distance were not captured in none of three performed experimental replicates, we hypothesize that suitable spatial orientation and accessibility of the amino acid and position of the modification are important factors for efficient cross‐linking (Section 2.13.9.1. in Supporting Information). Similar observations were made for HuR protein , in HIV‐RT all Cys were cross‐linked and are in proximity to RNA .Furthermore, covalent CA‐BSA‐Cy521RNA_1A (0 %), CA‐SSB‐Cy521RNA_1A (2 %), CA‐Gal1‐Cy521RNA_1A (0 %), CA‐lysoz‐Cy521RNA_1A (3 %) with exception of histone H2A that gave 9 % of CA‐H2A‐Cy521RNA_1A conjugate according to SDS‐PAGE analysis  and ImageJ quantification. Hence, the formation of covalent bond between modified RNA and the protein of interest requires strong proximity effect of a RBP and presence of Cys or His in the recognition site.To prove that the covalent bond between modified RNA and proteins is selectively formed due to proximity effect, we performed a control reaction with various weakly‐ or non‐RBPs containing Cys and/or His (Section 2.11.3 in Supporting Information). We tested bovine serum albumin (BSA), single strand binding protein (SSB), galectin 1 , lysozyme (lysoz.) and human recombinant histone H2A (H2A). These proteins gave no or only negligible conversions of cross‐linking products CA‐Cy536RNA_1A and non‐modified 36RNA_1A‐Cy5 with HeLa cell lysate. SDS‐PAGE analysis  revealed presence of several cross‐linked products in experiment with modified RNA, but not with unmodified control probe. Next, we aimed to elucidate, whether cross‐linking even in cell lysate is dependent on binding affinity. HuR protein is known to tightly bind AU‐rich sequences, while binding to CG‐rich RNA is significantly weaker.CA‐bind21RNA_3A (AU‐rich) and CA‐non‐bind21RNA_3A (GC‐rich) as well as two controls of unmodified probes 21RNA_3A‐bind (AU‐rich) and 21RNA_3A‐non‐bind (GC‐rich) (Section 2.3 in Supporting Information). Each of them was incubated with HeLa cell lysate. SDS‐PAGE followed by western blot analysis against HuR protein revealed presence of major slower moving band . This demonstrates that the cellular HuR protein was selectively cross‐linked to binding modified RNA probe, while modified non‐binding or non‐modified RNA probes remained unreacted. These experiments pave the way to further advanced applications of our toolbox in RNA proteomics and selective probes for target proteins.To demonstrate that our probes are functional in complex environment, we performed incubation of both modified in cellulo RNA proteomic studies, which are currently ongoing and for modifying the poly(A) tail in messenger RNAs with binding sites for a major class of regulatory factors, the poly(A) binding proteins.In conclusion, we designed and prepared modified 7‐deazaadenosine rNTP bearing highly reactive CA moiety, which served as a good substrate for T7 RNAP in enzymatic construction of various reactive RNA probes with one or several modifications at specific positions. (CA)‐modified RNA probes reacted with excess of thiol‐containing molecules and/or Cys‐ or His‐containing positively charged peptides to form stable covalent conjugates. (CA)‐modified RNA probes also reacted selectively with RBPs under physiological conditions and formation of covalent conjugates was highly enhanced by proximity effect. RNA‐protein cross‐linked conjugates were successfully characterized by intact mass analysis and proteomic nano‐LC–MS/MS. Our novel method enables to pinpoint exact RNA binding sites within a protein without any need of external cross‐linking reagents or UV source for activation of the reactive moiety for cross‐linking, thereby overcoming UV‐related damage and toxicity issues. It also could be useful for labelling of RNA with fluorophores, biotin or other tags. Most importantly, our study paves the way for further extension of this method to The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Candida species are one of the most concerning causative agents of fungal infections in humans. The treatment of invasive Candida infections is based on the use of fluconazole, but the emergence of resistant isolates has been an increasing concern which has led to the study of alternative drugs with antifungal activity. Sphingolipids have been considered a promising target due to their roles in fungal growth and virulence. Inhibitors of the sphingolipid biosynthetic pathway have been described to display antifungal properties, such as myriocin and aureobasidin A, which are active against resistant Candida isolates. In the present study, aureobasidin A did not display antibiofilm activity nor synergism with amphotericin B, but its combination with fluconazole was effective against Candida biofilms and protected the host in an in vivo infection model. Alterations in treated cells revealed increased oxidative stress, reduced mitochondrial membrane potential and chitin content, as well as altered morphology, enhanced DNA leakage and a greater susceptibility to sodium dodecyl sulphate (SDS). In addition, it seems to inhibit the efflux pump CaCdr2p. All these data contribute to elucidating the role of aureobasidin A on fungal cells, especially evidencing its promising use in clinical resistant isolates of Candida species. Candida species are one of the most concerning causative agents of fungal infections in humans. They are responsible for a wide spectrum of diseases, which range from superficial mycosis to life-threatening disseminated candidiasis [Candida albicans and Nakaseomyces glabrata (formerly named Candida glabrata) [Candida infections are the most common fungal disease associated with nosocomial acquired infections [didiasis . In the labrata) . The morlabrata) . In healfections .Candida infections was based on the use of fluconazole, an azole antifungal drug that blocks the enzyme lanosterol 14-α demethylase and, thus, disrupts ergosterol synthesis [The treatment of invasive ynthesis ,6. Howevynthesis . For thiynthesis ,9,10,11.ynthesis .C. neoformans, C. albicans, A. fumigatus and Pneumocystis murina, but does not affect the lipid metabolism in mammalian cells [Aspergillus and Candida species including Candida auris [Candida species [Scedosporium species, has revealed that many intermediate molecules from the biosynthetic pathway are affected when cells are treated with different inhibitors [Besides the class of azoles, there are only three other groups of antifungal molecules available in healthcare units, which target only three fungal cell structures: polyenes, which directly bind to ergosterol found in the plasma membrane and cause the release of cytoplasmic content; echinocandins, which inhibit the enzyme β-glucan synthase and affect cell wall synthesis; and fluoropyrimidine analogs, which block DNA synthesis . For thean cells . Inhibitda auris ,19,20,21da auris . Howeverhibitors .C. albicans and C. glabrata and their synergistic effect with fluconazole [Candida cells when treated with aureobasidin A. The present study aimed to elucidate the effect of aureobasidin A on fluconazole-resistant clinical strains of C. albicans and C. glabrata that overexpress MDR1, CDR1 and ERG11, which are typical mechanisms of resistance for azole antifungals, focusing its effect on fungal lipids and the interaction with antifungal drugs that target an important fungal lipid, ergosterol, such as fluconazole and amphotericin B.More recently, our group evidenced the antifungal effect of these inhibitors in fluconazole-resistant isolates of conazole . For theCandida strains were used in this study. C. albicans ATCC 10231D-5 and C. glabrata ATCC 2001D-5 were used as standard . Three clinical isolates displaying resistance patterns to fluconazole were also evaluated, C. glabrata 109 strain, C. albicans 1114 strain (both from our laboratory collection) and C. albicans 12-99 strain, kindly provided by Theodore White from University of Missouri, USA. For all experiments, the strains were grown on Yeast Extract Peptone Dextrose (YPD) agar and transferred to YPD broth and incubated at 37 °C for 18 h under agitation.A total of five Saccharomyces cerevisiae mutant strains were also used: AD/1234567, deleted from all genes that encode ABC transporters related to the MDR phenotype ; CaCDR1p+, CaCDR2p+, CaMDR1p+ and CgCDR1p+, that overexpress CaCdr1p, CaCdr2p, CaMdr1p and CgCdr1p, respectively, and present the same gene deletions as AD/1234567. Those mutants were kindly provided by Dr. Richard Cannon and Dr. Brian Monk .Aureobasidin A  and fluconazole  were used in susceptibility and synergism tests.Candida cells (1 × 107/mL) to each well of a polystyrene microplate and incubated for 1.5 h at 37 °C. Then, the supernatant containing non-adherent cells was removed and RPMI 1640 medium supplemented with MOPS, 2% glucose and 20% fetal bovine serum  was added in the absence (positive control) or presence of aureobasidin A (4–0.125 μg/mL) and/or fluconazole (256–258 μg/mL). Adherent cells were then incubated for 48 h at 37 °C.Biofilm formation was analyzed according to Hacioglu and colleagues . FirstlyFor the preformed biofilm assay, cells were grown as described above in the absence of the compounds. After 24 h of biofilm formation, the supernatant was removed and supplemented RPMI was added in the absence (positive control) or presence of aureobasidin A (4–0.125 μg/mL) and/or fluconazole (256–258 μg/mL). An additional incubation of 24 h at 37 °C was performed to evaluate the anti-biofilm activity.Evaluation of both biofilm formation and preformed biofilms was carried out using three chromogenic assays as previously described ,27: crysCandida cells were treated with both drugs using the checkerboard method and the results were evaluated after a 48 h incubation at 37 °C.Interaction analysis of aureobasidin A with antifungals was performed using the combination with fluconazole against preformed biofilms and with amphotericin B against planktonic cells. Both experiments were performed using the checkerboard method according to Reis de Sá and colleagues . For theThe fractional inhibitory index (FICI) was calculated according to the formula  + , where A is aureobasidin A and drug B is fluconazole. Interaction was classified according to the following parameter: ≤0.5, synergistic interaction; >0.5 to ≤4, no interaction; >4, antagonistic effect .Bliss independence model was performed according to Meletiadis and colleagues and Zhao and colleagues ,31. The Candida cells caused by aureobasidin A were analyzed using fluorescent staining. Oxidative stress, mitochondrial membrane potential and the content of chitin and neutral lipids were evaluated using 2′,7′–dichlorofluorescein diacetate (DCFH-DA) , JC-1 probe , calcofluor white  and Nile Red , respectively. Cells were grown in the absence (positive control) or in the presence of 0.5× MIC of aureobasidin A for 48 h at 37 °C. Then, cells were stained for 1 h at 37 °C under protection from light with 50 μg/mL of DCFH-DA, 10 μg/mL of JC-1, 25 μg/mL of calcofluor white or 2 mg/mL of Nile Red [Alterations of Nile Red ,33. Samp5 cells grown in 96-well plates containing supplemented RPMI in the absence (positive control) and in the presence of sub-inhibitory concentrations of aureobasidin A (0.5× MIC) for 24 h at 37 °C [The susceptibility to sodium dodecyl sulphate (SDS) was analyzed using 1 × 10at 37 °C . Then, tCandida cells (1 × 105) were grown in supplemented RPMI in the absence (positive control) or presence of aureobasidin A (0.5× MIC) for 48 h at 37 °C. After the incubation time, the cells were pelleted and the supernatant analyzed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) to quantify free DNA (260 nm). A sterile culture medium sample was used as a negative control.The cell membrane permeability was determined by measuring the release of DNA to the culture supernatant . CandidaS. cerevisiae cells (2 × 104/mL) were incubated in YPD broth in the presence of combinations of 2-fold serial dilutions of aureobasidin A (1.25–0.078 μg/mL) and fluconazole (500–1.95 μg/mL) at 30 °C for 48 h. Cell growth was measured using a 600 nm SpectraMax 340 microplate reader , and interactions were assessed through a FICI model, as described in The ability of aureobasidin A in inhibiting efflux pumps was evaluated through checkerboard assays. Briefly, Candida cells were grown in supplemented RPMI in the absence (positive control) or in the presence of 0.5× MIC of aureobasidin A and incubated for 48 h at 37 °C. After the period of incubation, the cell growth was collected and processed for scanning electron microscopy. Briefly, samples were processed as follows: i. fixation in 2.5% glutaraldehyde and 4% formaldehyde, in 0.1 M cacodylate buffer, for 30 min at room temperature; ii. post-fixation in 1% osmium tetroxide in 0.1 M cacodylate buffer containing 1.25% potassium ferrocyanide for 30 min; iii. dehydration in a graded ethanol series (30–100%); iv. critical point drying in CO2 ; v. adhesion to aluminum stubs with carbon tape; and vi. coating with gold.Scanning electron microscopy was performed according to Rollin-Pinheiro et al. . CandidaImages were obtained with FEI Quanta 250 scanning electron microscope  and processed using Photoshop v.CS6 software . The yeast area was measured using the Image J v.1.52a software.C. elegans strain that was acquired by the Caenorhabidtis Genetics Center (CGC), University of Minnesota (USA). The worms were maintained in nematoid growth agar medium (NGM)  at 15 °C, seeded with the strain of Escherichia coli OP50 that was used as the regular food source for these nematodes.The in vivo infection assay used AU-37 2PO4, 6 g of Na2HPO4, 5 g of NaCl, 1 mL of 1 M MgSO4 in 1 L of H2O) and vigorously vortexed with 0.5 mL of 5 M NaOH and 1.5 mL of 6% sodium hypochlorite in 35 s sequential cycles. Released eggs were then centrifuged at 1300× g for 50 s, cleaned in sequential washes in M9, added to plates containing NGM medium seeded with E. coli OP50 and incubated for 72 h at 25 °C to obtain synchronized larvae in stage L4 [C. albicans 12-99, C. albicans 1114 and C. glabrata 109 was carried out as proposed by Tempakakis and colleagues [5 cells of each strain were incubated in Petri dishes containing BHI agar supplemented with 90 µg/mL of kanamycin, 200 µg/mL of streptomycin and 200 µg/mL of ampicillin for 24 h at 37 °C. The plates were cooled to at room temperature and approximately 100 synchronized worms in stage L4 were added onto each plate seeded with different Candida strains and allowed to feed for 180 min at 25 °C and allowed to crawl on agar surface with the aim of feeding on yeast. Infected worms were then washed serially in M9 buffer and placed on clean BHI plates to crawl for 40 min, aiming to remove yeast from their cuticles [® plates  containing screen medium with 0.25 µg/mL aureobasidin alone or associated with 128 µg/mL of fluconazole and incubated at 25 °C for nine days maximum without shaking and scored as live or dead on daily basis. The survival of nematodes was monitored in an inverted microscope Axiovert100 Zeiss®  and determined through motility, sinus morphology and/or bulbopharyngeal movement of each helminth. The survival index was calculated from the proportion of the viable population in relation to the total number of worms, including live and dead animals.To assess the influence of 0.25 µg/mL aureobasidin A associated or not with 128 µg/mL of fluconazole on nematode infection caused by the three strains independently, Priya’s  methodolstage L4 . Infectilleagues , with micuticles . NematodAll experiments were performed in triplicate in three independent experimental sets. Statistical analyses were performed using GraphPad Prism v5.00 for Windows . The nonparametric Kruskal–Wallis one-way analysis of variance was used to compare the observed differences among the groups, and individual comparisons of the groups were performed using a Bonferroni post-test. The 90% or 95% confidence interval was determined in all experiments.t-test, and p values lower than 0.05 were considered significant.For the in vivo analysis, the experiment was conducted independently two-fold, with a quadruplicate analysis in each one. Data were analyzed by Student’s paired Candida strains used, the interaction between aureobasidin A and amphotericin B was indifferent, indicating that there was no synergistic or antagonistic effect between the compounds . Candida glabrata 109 and Candida albicans 1114 overexpress the CDR1 (ATP transporter) and MDR1 (MFS transporter) genes, respectively, whereas Candida albicans 12-99 overexpress the ERG11, CDR1, CDR2 and MDR1 genes [In previous studies from our group, it has been demonstrated that aureobasidin A is active against R1 genes .The present study aimed to investigate the effect of aureobasidin A against biofilms produced by these three strains. The evaluation of forming biofilm revealed that it did not affect fungal biomass A but redCandida isolates  was used as a stressor of plasma membrane to check whether the compound could increase the susceptibility of s to SDS .Considering that the treatment with aureobasidin A resulted in more susceptible cells, the permeability of fungal membrane in the presence of the compound was investigated by measuring the DNA concentration found in the supernatant of culture medium. Whereas the control cells displayed around 20 ng/μL of DNA, the treatment with aureobasidin A resulted in an increase in DNA content in the supernatant of 30–40 ng/μL , suggestS. cerevisiae mutant strains show that aureobasidin A displays antifungal activity against S. cerevisiae, with MIC of 1.25 μg/mL for the five strains tested. Fluconazole presented 0.5 μg/mL MIC against AD/1234567, 125 μg/mL against CaCDR2p+ and CaMDR1p+ strains, and 500 μg/mL against CaCDR1p+ and CgCDR1p+ strains. Combining aureobasidin A with fluconazole led to FICI values of 0.52–2.00 against the AD/1234567, CaCDR1p+, CaMDR1p+ and CgCDR1p+ strains, indicating indifferent interactions. The association between the compounds against CaCDR2p+ resulted in a synergistic interaction, with a FICI value of 0.28  and displays a synergistic effect with fluconazole [A recent study from our group has demonstrated that aureobasidin A is active against resistant clinical isolates of conazole . NeverthCandida biofilms was evaluated. Whereas it was active against biofilm formation, especially reducing the extracellular matrix and biofilm viability, a less prominent effect was observed against preformed biofilms. Tan and Tay [Candida biofilms and it has been shown that its susceptibility moved from 1 and 4 μg/mL in C. glabrata and C. albicans planktonic cells, respectively, to >64 μg/mL in biofilms of both species. Similar results have been observed for other Candida species [Since aureobasidin A is active against planktonic cells, as seen in the previous study, its effect on  and Tay  have als species . These dConsidering that aureobasidin A and fluconazole presented synergistic effects , the intCryptococcus neoformans was not affected in the presence of sorbitol, suggesting that the compound does not interfere with the synthesis of cell wall components [C. neoformans, which is responsible for the synthesis of galactoxylomannan of the fungal capsule, is decreased when cells are treated with aureobasidin A [Candida cells in our data; but, more studies are needed to elucidate the effect of aureobasidin A on cell wall components.Although it is well known that aureobasidin A acts on sphingolipid biosynthesis, there are few data in the literature showing modifications caused in fungal treated cells. Aureobasidin A MIC in mponents . Howeverasidin A . It suggCandida cells displayed increased oxidative stress and decreased mitochondrial membrane potential after aureobasidin A treatment, suggesting that the disruption of sphingolipid biosynthesis may also result in mitochondrial damage. In C. neoformans, it has also been shown that aureobasidin A treatment led to the production of oxidative stress, probably because of mitochondrial membrane disturbance and a reduction in lysosomal integrity [We found that ntegrity . It mighC. neoformans by transmission electron microscopy, in which cells displayed excessive vacuolization and deposition of lipid globules [Scanning electron microscopy revealed that treatment with aureobasidin A resulted in grouped cells compared to the control. Morphological alterations have also been demonstrated in globules .Candida membrane integrity. Aureobasidin A increased the susceptibility of treated cells to SDS and induced the release of DNA to the supernatant, suggesting that sphingolipids might play a role in membrane integrity and susceptibility to detergents. Similar results were found for other eukaryotic cells, such as Saccharomyces cerevisiae, C. neoformans and Toxoplasma gondii, in which crushed vacuoles, leakage of ions K+ and the loss of intracellular structures, respectively, were observed [Considering that aureobasidin A inhibits the synthesis of sphingolipids, which compose fungal membranes, it is reasonable to investigate in more detail the modifications it causes in observed ,41,42. AS. cerevisiae, deletion of ERG6 leads to resistance to aureobasidin A, indicating that alterations in genes responsible for ergosterol biosynthesis impact the susceptibility to sphingolipid inhibitors [C. albicans 12-99 strain, which overexpresses ERG11, is susceptible to aureobasidin A, suggesting that other genes from the ergosterol biosynthetic pathway are not related to the susceptibility to sphingolipid inhibitors. These data demonstrate that ergosterol and sphingolipid biosynthesis can be correlated.In hibitors . InteresC. albicans cells. Phospholipids and free fatty acids are the major lipids found in plasma membrane, followed by ergosterol and sphingolipids. Lipids are also present in the fungal cell wall, including ergosterol, fatty acid, phospholipids and sphingolipids, and its presence in the cell wall has been suggested to regulate C. albicans’ adhesion to host cells [Mishra and Prasad  have desst cells . These dC. albicans and C. glabrata, S. cerevisiae mutant strains that overexpress Candida MDR transporters were used. Since each S. cerevisiae strain harbors one specific efflux pump, it is possible to assess exactly which one is inhibited by a drug. Data showed that aureobasidin A inhibits CaCdr2p, due to the synergism observed. Moreover, combining aureobasidin A with fluconazole reduced the MIC of the antifungal by 64 times against CaMDR1p+, indicating that CaMdr1p is also inhibited by the same compound. It would explain the synergistic interactions between aureobasidin A and fluconazole against 1114, a C. albicans isolate that overexpresses CaMdr1p, and 12-99, a strain that overexpresses CaCdr1p, CaCdr2p and CaMdr1p [S. cerevisiae strains tested in this study, indicating that this substance is not a substrate for the transporters. Therefore, it may be presumed that: (1) efflux inhibition by aureobasidin A is not related to competitive inhibition at the catalytic site of the transporters; and (2) Candida spp. would not become resistant to aureobasidin A over time. Corroborating our data, it has already been demonstrated that the transporter Cdr1p is localized in enriched ergosterol and sphingolipid microdomains [IPT1 (inositol phosphotransferase) gene decreases the level of Cdr1p on plasma membranes [To evaluate whether aureobasidin A affects efflux pumps from  CaMdr1p . Moreoveodomains , and theembranes .C. elegans model of infection. For all of the three strains, the combination of aureobasidin A with fluconazole was effective in increasing the worm’s survival, corroborating the fact that aureobasidin A and fluconazole display in vitro synergism against Candida species. In vivo studies investigating the antifungal activity of aureobasidin A are rare in the literature. Takesako and colleagues [Candida infections. In addition, a previous study has shown that aureobasidin A is not toxic for C. elegans [The in vivo efficacy of aureobasidin A was assessed using the lleagues  have per elegans , indicatCandida cells, especially clinical resistant strains. Nevertheless, further studies are needed to test more Candida isolates and species, elucidate how sphingolipid biosynthesis is modulated by aureobasidin A and clarify how it interacts with antifungal agents. On the other hand, the evidence of an alternative drug with low MIC values, non-toxicity properties and in vivo efficacy to treat resistant isolates is a promising area to be explored to improve the management of non-susceptible invasive candidiasis in clinical settings.All the data presented here bring a more detailed analysis of how aureobasidin A affects


Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, can still infect populations in many countries around the globe. The Omicron strain is the most mutated variant of SARS-CoV-2. The high transmissibility of the strain and its ability to evade immunity necessitate a priority study of its properties in order to quickly create effective means of preventing its spread. The current research aimed to examine the in silico interaction between PIWI-interacting RNAs (piRNAs) and the SARS-CoV-2 genome (gRNA) to identify endogenous piRNAs and propose synthetic piRNAs with strong antiviral activity for drug development. This study used validated bioinformatic approaches regarding the interaction of more than eight million piRNAs with the SARS-CoV-2 genome. The piRNAs’ binding sites (BSs) in the 5′UTR were located with overlapping nucleotide sequences termed clusters of BSs. Several BSs clusters have been found in the nsp3, nsp7, RNA-dependent RNA polymerase, endoRNAse, S surface glycoprotein, ORF7a, and nucleocapsid. Sixteen synthetic piRNAs that interact with gRNA have been proposed with free binding energy ranging from −170 kJ/mol to −175 kJ/mol, which can be used to create drugs that suppress the reproduction of SARS-CoV-2. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, is still capable of infecting populations in many countries around the globe. SARS-CoV-2 causes disease in humans and many vertebrates ,4,5. SARNSP3 gene [Due to the absence of effective treatments for coronavirus strains, medications must be created that can both treat and stop the spread of infection. One of the properties of the coronavirus genome (gRNA) that contributes to its survival in animals lies in the structure of gRNA being similar to the mRNA structure of protein-coding genes. The gRNA nucleotide sequence contains 5′-untranslated region (5′UTR), protein-coding region (CDS), and 3′-untranslated region (3′UTR), which allows it to be similar to the mRNA structure of many genes, including being tens of thousands of nucleotides in length. A number of studies have examined the features of all of the functional regions of gRNA that could serve as the basis for the development of methods to prevent SARS-CoV-2. Since gRNA contains 5′UTR, there have been attempts to elucidate the role of this region of SARS-CoV-2 in the pathogenicity of the coronavirus. The viral 5′UTRs are believed to provide reliable expression of viral mRNAs. The above features can be used as potential therapeutic targets . The 5′USP3 gene ,29,30. RSP3 gene , and theSP3 gene  have beeSP3 gene . The expSP3 gene . The devSP3 gene .Papain-lSP3 gene . SurprisSP3 gene . The aboSP3 gene , but theSP3 gene ,40. ReceSP3 gene ,42. In rSP3 gene ,44. It hSP3 gene . This inhttps://www.ncbi.nlm.nih.gov/, accessed on 17 January 2022). From Wang et al., 8.426 million piRNAs’ nucleotide sequences were taken [The nucleotide (nt) sequence of the Omicron variant (B.1.1.529) of the SARS-CoV-2 gRNA has been obtained from the National Center for Biotechnology Information (NCBI) (re taken .The BSs of piRNAs in SARS-CoV-2 gRNA were predicted using the MirTarget program . This prLEP gene, consider the diagram of piRNAs binding to the mRNA site in LEP gene forms a helix almost identical to the helix containing only canonical pairs of nucleotides. Below is a scheme of the fully complementary interaction of all piR-23387 nucleotides with the mRNA LEP gene. The piR-110734 complex with the mRNA FKBP5 gene forms a secondary and tertiary structure almost identical to the secondary and tertiary structures when only canonical nucleotide pairs are used.As an example of the interaction of piRNAs with the mRNA The first region of the gRNA nucleotide sequence that interacted with piRNAs was located at the 5′UTR . The BSsThe overlap of the nucleotide sequences of piRNA BSs in gRNA leads to the competition of different piRNAs while suppressing the start of the process of translation of coronavirus proteins. Given that the genes encoding these piRNAs can be located in different parts of the human genome, in the case of mutations that disrupt the synthesis of one of these piRNAs, the body will retain the ability to defend itself against the coronavirus. In addition to the above four piRNAs interacting with gRNA with free energy from −130 kJ/mol and higher, in the 5′UTR BSs cluster at position 156 nt, piR-2388809 also bound with a ΔG value of −132 kJ/mol . TherefoIn the nsp3 protein-coding part of gRNA, we identified several clusters of BSs for four or more piRNAs . ConsideThe third cluster of piRNA BSs from 7137 nt to 7161 nt was also located in the nsp3 protein-coding region of gRNA . A featuThe BSs cluster of four piRNAs with gRNA is shown in The largest number of piRNAs bound in the BSs cluster was located from 12,029 nt to 12,055 nt in the nsp7 protein-coding region of gRNA . The nucAll nucleotides of four piRNAs bound to gRNA to form canonical and non-canonical pairs, as shown in In the region from 20,598 nt to 20,624 nt of gRNA, there was a BSs cluster of eighteen piRNAs that inhibit endoRNAase synthesis . All theThe next piRNA BSs cluster from 24,308 nt to 24,338 nt was located in the gRNA region encoding the S surface glycoprotein . All theBy replacing nucleotides forming non-canonical pairs with nucleotides forming canonical pairs in piRNAs and replacing non-interacting nucleotides, the resulting spiRNAs bound to gRNA with free energy from −152 kJ/mol to −165 kJ/mol. Such large values of ΔG are also associated with the length of piRNAs from 28 nt to 31 nt. These spiRNAs, fully complementary to gRNAs, are identical in function to widely used siRNAs that cause inhibition of viral RNA protein synthesis ,52,53. TThe gRNA region from 27,375 nt to 27,399 nt encodes the ORF7a protein . Most ofIn the gRNA region encoding the N protein, a cluster of piRNA BSs from 28,447 nt to 28,471 nt was detected . All piRThe last cluster of piRNA BSs was located in the region from 29,280 nt to 29,308 nt of gRNA, which also encodes the N protein . Before The clusters of piRNA BSs in gRNA that we have identified form the basis for selecting the most effective piRNAs that can be used to suppress SARS-CoV-2 proliferation. From the list of piRNAs  that intCoronavirus has been found in many animal organisms, which indicates the early occurrence of their protective methods to fight this pathogen ,57,58. IOf the 8,426,000 piRNAs, 92 piRNAs could bind to gRNA with a value of −130 kJ/mol or more . These pSingle nucleotide mutations in BS clusters cannot significantly protect the coronavirus from piRNAs. However, substitutions of three or four nucleotides in the piRNA BSs cluster with a length of 30–34 nucleotides can significantly reduce their impact on coronavirus reproduction. Therefore, during the evolution of animals, they selected longer piRNAs to protect against coronavirus. For this reason, miRNAs, which are much shorter than piRNAs, are less used by animals to protect against coronavirus. The clusters of long piRNA BSs in gRNA that we found are an indicator of the protective function of the body.In addition to BSs clusters of long piRNAs in gRNA, we found BSs clusters for 24–28 nt long piRNAs. Dozens of piRNAs of this length can bind in these clusters. The best example of this is the cluster of piRNA BSs in the gRNA region from 12,029 nt to 12,055 nt . Such a The data obtained in this work on the effect of piRNAs on coronavirus can be used to predict the likelihood of infection with coronavirus and in the development of drugs based on endogenous piRNAs and spiRNAs. It is logical to create spiRNAs based on piRNAs that are 30–34 nt long that interact with gRNAs with high free energy and bind in the gRNA regions encoding the most important protein components of the coronavirus. At the same time, these highly binding piRNAs will also interfere with gRNA replication.


Important decisions often involve choosing between complex environments that define future item encounters. Despite its importance for adaptive behavior and distinct computational challenges, decision-making research primarily focuses on item choice, ignoring environment choice altogether. Here we contrast previously studied item choice in ventromedial prefrontal cortex with lateral frontopolar cortex (FPl) linked to environment choice. Furthermore, we propose a mechanism for how FPl decomposes and represents complex environments during decision making. Specifically, we trained a choice-optimized, brain-naive convolutional neural network (CNN) and compared predicted CNN activation with actual FPl activity. We showed that the high-dimensional FPl activity decomposes environment features to represent the complexity of an environment to make such choice possible. Moreover, FPl functionally connects with posterior cingulate cortex for guiding environment choice. Further probing FPl’s computation revealed a parallel processing mechanism in extracting multiple environment features.  •FPl and vmPFC are involved in environment choice and item choice, respectively•FPl and a convolutional neural network (CNN) share similar neural representations•Analyzing the CNN suggests FPl extracts environment features via parallel processing•FPl functionally connects with dPCC for guiding environment choice Neuroscientists thought that the ventromedial prefrontal cortex (vmPFC) is involved in signaling all kinds of decisions. Here, Law et al. reveal that the frontopolar cortex (FPC), rather than vmPFC, is involved when people are choosing between complex environments. Artificial intelligence showed that FPC extracts complex choice features via parallel processing. Big decisions often involve selecting between abstract options that constrain future environments and potential choices within them. For example, when searching for a new house, one may begin by choosing between residential areas or price ranges, pre-selecting or filtering out many a set of potential options in favor of another, before deciding between the actual houses. Here, we refer to such a pre-filtered set of options as an “environment,” which comprises a large number of houses (referred to as “items”).,,Selection of an environment  has important long-term consequences of determining the concrete items  the chooser will encounter. In ancient times, a suboptimal environment choice could even be disastrous . Importantly, the computational demands of making environment choices are qualitatively distinct from item choices. Compared with item choice, environment choice involves higher levels of complexity (as the choice information is more multiplex), abstraction , and reward prospect (as the choice consequence is more prolonged). In particular, to make more adaptive decisions, retention of complex information is vital in environment choice. A good way to achieve this is through decomposition of the complex information into a few highly informative dimensions. However, despite the related findings on the information selection process in decisions involving numerous choices,We are equally ignorant about the neural mechanisms underlying environment choice, despite its evolutionary relevance, unique computational needs, and distinct behavioral responses. Previous studies trying to identify the brain regions responsible for option evaluation often required participants to choose between concrete items, such as food or probabilistic gambles. Then they computed the overall decision value (DV) to correlate it with brain activity under the assumption that any region evaluating the offers should have scaled activity linked to the decision/comparison signal itself.Applying neural networks can help in solving this problem. First, it provides a data-driven DV estimate that can help identifying a region of interest (ROI) for further analyses. Second, and critically, the high-dimensional architecture of a neural network allows further testing of whether the ROI and neural network share a similar computational mechanism. While there are many neural network architectures, here, convolutional neural networks (CNNs) are of specific interest. This is because CNNs are widely used in computer vision for recognizing objects from complex visual information. Normally, it involves parallel processing for extracting multiplex features from the data, which is particularly useful for studying environment choices that are characterized by multifaceted choice information.,,,,,,,,,,,A good candidate region for environment choice in humans is lateral frontopolar cortex (FPl), as it has been implicated in related cognitive processes, such as abstract reasoning, information integration, and prospective memory.Importantly, any brain region involved in environment choice should pass the following three tests. First, for making comparisons between environments, the region should contain a “value comparison signal,” a signal that scales with the difference in value between environments. Second, the region’s value comparison signals should be sensitive to contextual factors that alter choices. Third, the signal should be linked to value itself, instead of confounding factors such as salience of the environments.In this study, we designed a two-stage decision-making task to investigate the neural mechanisms underlying environment choice using functional magnetic resonance imaging (fMRI). Stage 2 of the task was a typical decision-making task that involved choosing between two items. The preceding stage 1 involved an environment choice that determined items that were subsequently available in stage 2 for selection. To extract environmental features and overall DV, we trained a behaviorally optimized and brain-naive CNN. In a series of univariate analyses and representational similarity analyses (RSA) comparing the model elements with neural activity, we observed dissociable patterns of activity in FPl and vmPFC in environment choice and item choice. In addition, we showed that the computation of FPl is related the extraction of environment information and the computation of dorsal posterior cingulate cortex (dPCC) is related to the combination of such information for guiding choices. This functional network between FPl and dPCC was also confirmed in a psychophysiological interaction (PPI) analysis. Further probing FPl’s computation revealed a more specific mechanism of parallel processing in extracting multiple environment features for decision making. These results suggest FPl extracts multiple choice features and passes information to dPCC for guiding environment choice.To examine the neural mechanisms of environment choice, we developed a two-stage decision-making task A and 1B.,In addition, to test whether the neural decision signals were flexibly modulated under different contexts, we introduced two different bonus conditions ), participants were more likely to choose the rightward environment  × Bon × Cond three-way interaction: β = 0.271, t23 = 3.065, p = 0.006; A general linear model . An additional analysis showed that the preference for larger variance was not confounded by any exploratory behaviors due to unfamiliarity of the task  to guide decisions. We then carried out GLM2 see  to test the task E. To conthe task A. Consisthe task B.,So far, applying GLM1 held an assumption that participants perfectly estimated the EVs of the options and made their choices accordingly. However, each environment consisted of information from 20 items, and it is unclear how the integration of this complex information occurred computationally. Similar to environment choice, visual object recognition also involves integrating complex information from the retina. In artificial intelligence, the CNN is successful in computer vision not only because it can decompose complex visual information and perform nonlinear integration to decode image identity but also because it resembles the hierarchical neural process of the human ventral visual pathway.ts38 < −2.661, Ps < 0.012; We tested carefully different CNN architectures before analyzing the fitted data further see Aiii invots38 < −3.226, Ps < 0.003; t38 = −0.345, p = 0.732).The same set of models was applied to find the model that best describes participants’ item choices for subsequent neural analyses . Notably, the input for item choices only contains values of two items, as opposed to that for environment choices, which involves information of 40 items. Convolution is not applicable for item choice, and the CNN for environment choice was reduced to an artificial neural network (ANN) by the absence of a convolution B. Intere,,,,Env) and the DV difference between items . Interestingly, a dissociation in the DV difference signals of the two stages was observed. First, we replicated a widely reported finding that vmPFC activity correlated with ΔDVItem in item choice  = 16.026, p = 0.001). Taken together, the findings provided further evidence that, in FPl, a DV difference signal was only present during environment choice and was absent during item choice, whereas the vmPFC showed the opposite pattern.Despite the double dissociation of FPl and vmPFC in environment choice and item choice, it is equally important to ascertain that the absence of signal in FPl and vmPFC was not merely due to the conservative statistical corrections in the whole-brain analysis. Hence, an ROI analysis was performed by placing a mask over these regions and testing their signal time courses. The results were broadly consistent with those from the whole-brain analysis. In environment choice, the ΔDV,,,Z > 3.1, p = 2.44 × 10−13; Z > 3.1, p = 1.73 × 10−24; Env in environment choice , which exhibits DV signals during item choice and foraging.β = 0.048, t23 = 2.529, p = 0.019, BF10 = 2.869; stage 1 connectivity = 0.457; stage 2 connectivity = 0.410; β = −0.081, t23 = −2.388, p = 0.026, BF10 = 2.227; stage 1 connectivity = 0.298; stage 2 connectivity = 0.379; So far, we observed that dPCC was co-activated with FPl and vmPFC in environment choice and item choice respectively. Next, we tested the functional coupling among these regions using a PPI analysis Ci. The rEnv and vmPFC ΔDVItem signals must possess at least two additional properties: (1) it should be flexibly modulated only by choice-relevant information ; (2) it should be orthogonal to the option (environment or item) salience. We report two sets of analyses below that demonstrated FPl ΔDVEnv signal and vmPFC ΔDVItem signal possessed these two properties.Our neural analyses so far suggest that FPl has a specific role in environment choice, whereas vmPFC has a specific role in item choice. To confirm the signals were not confounded by other factors, FPl ΔDVBasic) and two DV terms that only concerns the bonus value in the linked condition (DVLinked Bonus) or in the unlinked condition (DVUnlinked Bonus). The DVUnlinked Bonus term served as a control to test that the neural signals were not modulated when the bonus was irrelevant to the choice. Not surprisingly, during environment choice, FPl encoded the ΔDVBasic . This was possibly due to the repetition suppression of the bonus signal when the bonus remained the same on subsequent item choice trials.As participants flexibly adapted their choice according to the bonus conditions A, we tesEnv even though the factor of salience was included  between environments but only the partial DV of detector 1 is correlated with difference in skewness . Interestingly, the correlation patterns varied across feature detectors. For example, detector 1 is more strongly related to mean difference compared with those of other detectors , while detector 3 is more related to variance difference . In short, the differential correlation patterns associated with the feature detectors enable the CNN to extract information from the item distributions during environment choice.So far, we found that FPl is important to environment choice by showing a signal correlated with the CNN-derived DV parameter. Behavioral analysis results also showed that environment choices were partially guided by statistical moments of the environments’ item distribution C. Howeverhos > 0.978; zs > 4.413, signed-rank Ps < 1.017 × 10−5, permutation Ps < 0.001; rho = 0.996; zs > 4.413, signed-rank Ps < 1.017 × 10−5, permutation Ps < 0.001). These suggest the CNN for environment choice is most similar to a model that receives input of the mean, variance, and skewness of the environments.To find out whether the CNN does make use of statistical moments to guide environment choice, we tested the representational similarity of the CNN with five “simplified” CNN models that are trained with explicit information about statistical moments. Instead of receiving 20 component items of each environment as input, these simplified models receive either the item mean (mean model), the item mean and variance (mean + Var model), item mean and skewness (mean + skew model), item variance and skewness (Var + skew model), or item mean, variance, and skewness (mean + Var + skew model). Supposedly, the original CNN resembles the fifth model the most, and that was exactly what we found in an RSA. There were significant correlations in the RSAs between the original CNN and these five simplified CNN models . To begin with, we compared the FPl activity with the full CNN . First, we calculated the dissimilarity in multi-voxel activity of FPl  between each pair of trials in the form of representational dissimilarity matrix (RDM). Second, we tested the similarity in RDMs between FPl and CNN via a Spearman correlation. Note that, during the RSA, the average signal of a brain region is essentially removed, such that the RSA should be orthogonal to the univariate analysis in rho = 0.003, signed-rank p = 0.056, permutation p = 0.040; rhos > 0.007, signed-rank Ps < 0.035, permutation Ps < 0.024; rhos < 0.004, signed-rank Ps > 0.317, permutation Ps > 0.139). Intriguingly, the fully connected layer (which combines choice information into DVs) was correlated with dPCC only . On top of the functional coupling between FPl and dPCC . In addition, the similarity was greatest to the CNN with four feature detectors, which is the same CNN that was mainly analyzed in this study . Taken together, these results suggest FPl adopted a parallel processing for the encoding of environment information.Finally, we tested more specifically whether FPl involved a parallel processing to extract environment information, which is an important component of the CNN architecture. We systematically varied the number of CNN feature detectors from 1  to 10. Then, we compared the extracted environment information  of each CNN variant with FPl using an RSA. We found that FPl shared similar representations to all CNN variants with 2–10 feature detectors in parallel  that were visually different. Since environment choice, by nature, involves more complex information than item choice, it is almost unavoidable that environments need to be represented using more complex visual stimuli. Ideally, the environment and item choices should include identical sets of visual stimuli. One solution was to ask people to first learn the associations between abstract stimuli and the environment/item choice information and then use those abstract stimuli for the subsequent environment/item choice experiment. However, such a design would be cognitively demanding to participants and was avoided in the current study. Another limitation of the current study is that the functional significance of the FPl’s parallel processing is not clear. The feature of parallel processing has been implicated in studies that focused on FPl, such as maintaining multiple goals during multi-tasking.kelvinck.law@gmail.com).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Chun-Kit Law , aged 19–39 years with normal or corrected-to-normal vision and no current or history of neurological or psychiatric conditions, were recruited via advertisement in the university and participants’ referral. Written informed consent for each participant was obtained before experiment. This experiment was approved by the Human Subjects Ethics Committee of The Hong Kong Polytechnic University. One participant withdrew because of contraindications to MRI scans. Data of another participant was excluded because the fMRI session had to split into two runs and it was not possible to be analyzed in the same way as the data of other participants. Hence, data of twenty-four participants were analyzed in this study.Participants underwent a two-stage decision making task A and 1B.,Neural signals related to choice values are sometimes confounded by choice salience because they are collinear when only rewarding options are included in an experiment.Human choice is flexible to contextual demands and neural signal that reflects choice values should also be flexibly modulated in the same manner. To examine the flexibility of neural signals related to environment and item choices, there were two Bonus Conditions in this task C. In eact23 = 5.971, p = 4.355 × 10−6), while that in Stage 2 trials remained similar .Before fMRI scanning, all participants underwent a training session to get familiar with the experimental task. First, the experimenter explained the task instruction in details, including how the reward magnitude and probability of environments/items were represented, the relationship between Stage 1 and Stage 2, the differences between conditions , and the way of obtaining the bonus. Participants were explicitly told that the final incentive depended on the choice outcomes of all trials in the experiment. Afterward, participants underwent a practice task which was a shortened version of the actual experimental task . The choice accuracy in Stage 1 trials was significantly greater in the actual experimental session than that in the training session (P(R) denotes the probability of choosing the rightward option. Prob(R-L) denotes the difference in reward probabilities (i.e. the average environment probability in Stage 1 or item probability in Stage 2) between the rightward and leftward options; EV(R-L) denotes the difference in expected value (reward magnitude multiplied by reward probability) between the rightward and leftward options; Bon and Cond represent the bonus value and Bonus Condition  respectively.To test whether participants’ choices (to the right = 1 and left = 0) were biased by the options’ expected value and probability, bonus value, and Bonus Condition, General Linear Model (GLM) 1 (GLM1) was applied to analyze the data of Stage 1 environment choice and Stage 2 item choice separately:Mean(R-L), Variance(R-L), Skewness(R-L) denote the differences in mean, variance, and skewness of environment probability respectively. A one-sample t test was performed to examine whether each beta weight was significantly different from zero across participants in both GLMs.Each Stage 1 environment consisted of a distribution of twenty items. To test whether participants’ environment choices were biased by the statistical moments of the item distributions, GLM2 was performed:X is the reward probability matrix with a size of I×J; W is the matrix of the feature detector weights with a size of M×N and W(1)mn refers to the element in the mth row, nth column; b(1)f is the bias term for the fth feature map; The row and column of the feature map are denoted by i and j respectively .Environment choice requires integration of complex information about item distribution. One characteristic of the convolutional neural network (CNN) is its capability to integrate bundles of information into digested feature representation. Therefore, we employed the CNN to investigate how complex environment values were evaluated and integrated to give rise to decisions. There are four essential layers in the CNN: (1) input layer; (2) hidden layers; (3) choice-predicting fully-connected layer; (4) output layer Ai. Firstconcat; concat is vectorized in the first fully-connected layer (fc1) and transformed into 512 nodes and then activated by a rectified linear unit (ReLU) function:W(2)N and b(2)N are the weight matrix and bias term for the Nth node respectively. In the subsequent choice-predicting fully-connected layer , all nodes are concatenated into one column vector and then weigh-summed to form the decision values (DVs). The DVs for the kth environment are computed as follow:W(3)k and b(3)k are the weight matrix and bias term for the kth environment respectively. Finally, the choice probability of each environment is computed by applying a softmax function to the DVs in the output layer:After convolution, the feature maps related to the reward probabilities, the reward magnitudes, the product between the feature maps and reward magnitudes, and bonus information are concatenated  was applied to model Stage 2 item choice behavior Bi. It isDVR is the DV of the rightward option (environment in Stage 1 or item in Stage 2); Mag(R-L) denotes the difference in reward magnitude between the rightward and leftward options. All other variables are identical to those included in GLM1: Prob(R-L) denotes the difference in reward probabilities between the rightward and leftward options; Bon and Cond represent the bonus value and Bonus Condition  respectively.In addition to the CNN, we performed five additional models to identify the model that provides the best account of participants’ choice behavior. The first one is the GLM which was adapted from GLM1, the DV was fitted using the following equation:,,,kth environment (either the environment on the left or right); Probkq denotes the qth individual item probability in the kth environment; ρ and σ are free parameters that denote the preferences for variance and skewness respectively. DV of the kth environment is the product of its reward magnitude (Magk) and subjective probability:The MVS modelg. DV of the kth environment is identical to that of MVS model.Power law was shown applicable to the estimation of the average value of a distribution.Magk is the reward magnitude of the kth option and α and β respectively whereas the degree of loss aversion is indicated by λ. Besides, subjective probability under gains and losses is estimated via two weighting functions:γ and Δ respectively. Computation of DV of the kth option is similar to Equation 3 except subjective reward magnitude h is the weight matrix for the hth node; b(4) is a bias term. The DV for the kth option is computed by:W(5)k and bk(5) are the weight matrix and the bias term for the kth option respectively.Apart from the CNN, we applied another deep learning neural network, the autoencoder, to fit participants’ choices. Similar to the CNN, option value in terms of reward magnitude (Mag), reward probability (Prob), bonus value (Bon), Bonus Condition (Cond), expected value (Mag×Prob), and Prob×Bon×Cond are fed in as the input of the autoencoder. The autoencoder transforms the input into 512 hidden nodes and each hidden node is activated by a sigmoid function:Note that we tested different autoencoder architectures by systematically varying the number of hidden nodes , as in what was performed for the CNN , and theAll CNNs (Stage 1 and Simplified) and ANN were fitted to participants’ choices using MATLAB Deep Learning Toolbox  whereas the alternative models were fitted using MATLAB VBA-toolbox.3 voxel-resolution, TR = 1.6s, TE1 = 15ms, TE2 = 36.19ms, TE3 = 57.38ms, flip angle = 70°, slice angle = −30° (T>C). Field maps were collected to correct for signal distortions using a dual echo 2D gradient echo sequence: 2.5 × 2.5 × 2.5 mm3 voxel resolution, TR = 590ms, TE1 = 4.92ms, TE2 = 7.38ms. T1-weighted structural images were acquired using an MPRAGE sequence: 1 × 1 × 1 mm3 voxel resolution, 174 × 192 × 192 grid, TR = 1900ms, TE = 3.97ms, TI = 904ms.Neuroimaging data were collected by a Siemens 3T MRI scanner. FMRI data was collected with a multi-band echo planar imaging sequence: 2.5 × 2.5 × 2.5 mmFMRI data was analyzed using FMRIB’s Software Library (FSL).Env (i.e. difference in DV between the chosen and unchosen Stage 1 environments) and ΔDVItem (i.e. difference in DV between the chosen and unchosen Stage 2 items). These terms were time-locked to the onset time when the environments/items were offered and convolved with a haemodynamic response function. Six additional nuisance regressors were also included. One parametric regressor related to the bonus value, time-locked to block bonus onset, was included. One parametric regressor related to the value of the chosen environment, time-locked to the onset of Stage 1 delay phase, was included to model the neural signal related to the chosen environment prior to the onset of each Stage 2 trial. Two box car regressors related to left-hand and right-hand responses, time-locked to participants’ decisions, were included. Finally, two regressors related to the average BOLD signal in the cerebrospinal fluid (CSF) and white matter (WM) were included. At group level, FMRIB’s local analysis of mixed effects,All whole-brain analyses were performed using a univariate GLM approach. To investigate the value signals during environment choice and item choice, GLM3 was applied to analyze the fMRI data of individual participants, which involves the following two regressors: ΔDV,ROI analysis was performed to scrutinize the time courses of the key brain regions identified from the whole-brain analysis. To avoid circularity, all ROIs were located based on the findings of previous studies that revealed similar cognitive processes. From GLM3, we found that signals in the lateral frontopolar cortex (FPl) and the ventromedial prefrontal cortex (vmPFC) were related to ΔDV during environment choice and item choice respectively. A mask was created to extract the activity of the FPl by centering a sphere of 3mm radius at the coordinates that is implicated in directed exploration,Basic); (2) a DV term estimated using only the bonus value in the Linked Condition (DVLinkedBonus) and; (3) a DV term estimated using only the bonus value in the Unlinked Condition, but the bonus was included as if it was in the Linked Condition (DVUnlinked Bonus). For vmPFC in Stage 2 item choice  were extracted. Next, a self-correlation was performed using Pearson correlation and then subtracted from 1 to become an RDM of that layer. On the other hand, for fMRI neural data, RDM computation follows the same procedures except that trial-by-trial voxel activations in different ROIs were extracted instead. In addition to the FPl, voxel activation of other ROIs and control regions, namely the vmPFC, dPCC, primary visual cortex (V1), and CSF, were also extracted. Voxel activation extraction for V1 and CSF was carried out using atlases available in FSL.It is worth noting that one participant did not fully complete the decision making task within an fMRI run such that there were fewer number of trials for neural data than behavioral data. When RSA between neural RDM and CNN RDM was performed for that participant, the CNN RDM was computed excluding the trials that lacked fMRI data.Twenty healthy young adults , aged 18–27 years with normal or corrected-to-normal vision and no current or history of neurological or psychiatric conditions, were recruited via advertisement in the university and participants’ referral. Written informed consent of each participant was obtained before experiment. The experiment was approved by the Human Subjects Ethics Committee of the Hong Kong Polytechnic University.It was a simplified environment choice task, which is similar to the Stage 1 of the main experiment A. In eac


Immune checkpoint inhibitors (ICIs) are pharmaceutical agents capable of disrupting immune checkpoint signaling, leading to T-cell activation and a robust anti-tumor response . The firPDL-1 expression, as assessed through immunohistochemistry (IHC), was the first marker to gain approval and practical application in conjunction with ICIs. Patients with high PDL-1 expression (PDL-1 > 50%) tend to exhibit a more favorable response to the treatment. However, intriguingly, certain patients with lower PDL-1 expression  have demonstrated enhanced ORR and prolonged survival, while some with elevated PDL-1 expression exhibited resistance to therapy ,12. FurtTumor mutational burden (TMB) quantifies the number of mutations harbored by tumor cells within a specific neoplasm. This mutational burden is closely associated with antigen processing and load in MHC-I, consequently impacting anti-tumoral immunity. For this instance, the study of Robert M. Samstein et al. demonstrated a positive correlation between high TMB and efficacy of ICIs across diverse cancer types and for different types of ICIs, as revealed by better ORR and longer survival in a robust cohort of 1662 cancer patients treated with different ICIs  . This coThe genetic variation in the DNA mismatch repair (MMR) pathway is one source of a high TMB load, which can lead to genome instability and microsatellite instability (MSI). For instance, as revealed by the Phase II CheckMate 142 Study, the application of ICIs in metastatic colorectal cancer (mCRC) demonstrated that defects in MMR and/or MSI (dMMR/MSI-H) are predictive indicators for the effectiveness of Pembrolizumab, Nivolumab, and Nivolumab in combination with low-dose Ipilimumab ,22. NotaIn the pursuit of identifying alternative biomarkers, researchers have directed their efforts towards analyzing intermolecular interactions within tumor cells . This exA majority of the biomarkers currently under investigation encompass cellular factors or molecules intricately engaged in immune evasion. These components operate within a coordinated framework, either fostering an environment favoring anti-inflammatory processes or contributing to the creation of a suppressive tumor niche that hampers anticancer immunity, ultimately culminating in resistance to ICIs ,36. The In conclusion, the collaborative efforts of researchers and clinicians in deciphering the intricacies of immune responses offer a pathway to personalized treatment strategies driven by biomarkers. This holds the potential to enhance the effectiveness of cancer therapy and ultimately lead to better patient outcomes. A holistic endeavor towards validating, standardizing, and incorporating biomarkers will pave the road towards achieving precision medicine within the realm of immune checkpoint inhibitors and cancer treatment.


Metastasis remains the major cause of treatment failure in patients with nasopharyngeal carcinoma (NPC), in which sustained activation of the Notch signaling plays a critical role. N6-Methyladenosine (m6A)-mediated post-transcriptional regulation is involved in fine-tuning the Notch signaling output; however, the post-transcriptional mechanisms underlying NPC metastasis remain poorly understood. In the present study, we report that insulin-like growth factor 2 mRNA-binding proteins 3 (IGF2BP3) serves as a key m6A reader in NPC. IGF2BP3 expression was significantly upregulated in metastatic NPC and correlated with poor prognosis in patients with NPC. IGF2BP3 overexpression promoted, while IGF2BP3 downregulation inhibited tumor metastasis and the stemness phenotype of NPC cells in vitro and in vivo. Mechanistically, IGF2BP3 maintains NOTCH3 mRNA stability via suppression of CCR4-NOT complex-mediated deadenylation in an m6A-dependent manner, which sustains Notch3 signaling activation and increases the transcription of stemness-associated downstream genes, eventually promoting tumor metastasis. Our findings highlight the pro-metastatic function of the IGF2BP3/Notch3 axis and revealed the precise role of IGF2BP3 in post-transcriptional regulation of NOTCH3, suggesting IGF2BP3 as a novel prognostic biomarker and potential therapeutic target in NPC metastasis. Nasopharyngeal carcinoma (NPC) is a metastasis-prone malignant tumor arising from the nasopharyngeal epithelium, in which approximately 70% of patients present with locoregionally advanced NPC at first diagnosis –3. DespiThe development of metastasis is a highly inefficient multistage process, in which organ colonization is regarded as the most complicated and rate-limiting step . DissemiN6-Methyladenosine (m6A) is one of the post-transcriptional types of multiple cancers . The gloHerein, we observed that the m6A reader IGF2BP3 was upregulated in NPC tissues and IGF2BP3 overexpression was an independent predictor of poor prognosis in NPC patients. Ectopically expressed IGF2BP3 expression in NPC cells identifies and binds with m6A-modified NOTCH3 mRNA to facilitate its stability and inhibit deadenylation-mediated mRNA decay, which constantly activates the Notch signaling pathway and enhances the tumor-initiating activity, eventually promoting tumor metastasis. In general, our findings shed light on the mechanism of tumor metastasis in NPC and suggest a novel therapeutic strategy against NPC tumor metastasis.P = 0.037), advanced N stage (P = 0.005), tumor metastasis (P = 0.001) and patient death (P = 0.008)  of the indicated cells into nude mice to evaluate the function of IGF2BP3 in regulating the cancer stem cell (CSC) properties of NPC in vivo. IGF2BP3 upregulation contributed to markedly higher tumor incidence and faster xenograft outgrowth. Conversely, the IGF2BP3-silenced cells presented lower tumor incidence and slower xenograft outgrowth  predicted five high confidence m6A sites within the NOTCH3 CDS   tails , 34. To F1) Fig. . FurtherF1) Fig. . MoreoveFinally, we examined whether the IGF2BP3/Notch3 axis was clinically relevant in NPC. IGF2BP3 and N3ICD levels were potently upregulated in NPC primary tumor samples from the patients with metastatic NPC compared to those without metastasis, suggesting the significant relevance of the IGF2BP3/Notch3 axis in NPC Fig. . FurtherIn summary, we here show that IGF2BP3 facilitates NOTCH3 mRNA stability to promote Notch3 signaling activation, then enhances the survival and tumor-initiating activity in disseminated tumor cells, leading to robust tumor metastasis in NPC Fig. . This stA high potential for distant metastasis results in the majority of treatment failure and deaths in patients with NPC –5. ThereMUC4/Y, one of the MUC4 transcript variants, was reported to be overexpressed in pancreatic cancer, in which it upregulated NOTCH3 expression, promoting tumor angiogenesis and metastasis [As an evolutionarily conserved mechanism, the Notch3 pathway is one of a major signaling axis in the maintenance of the stem-like phenotype, in which the functions of Notch signaling are not limited to triggering tumorigenesis and enhancing drug resistance, but instead exert multiple pivotal functions in tumor metastasis , 35–39. tastasis . In the GSIs are a major class of Notch inhibitors, which prevent the final proteolytic cleavage of Notch receptors that releases the NICD . It has NOTCH1 [NOTCH3 and promotes its mRNA stability via decreasing the interaction between NOTCH3 and the CCR4-NOT deadenylase complex, leading to constantly activated Notch3 signaling. Our findings reveal a novel post-transcriptional mechanism for the activation of Notch3 signaling.Besides post-translational regulation, given that adenine/uridine-rich elements (AREs) occur in the 3′ UTR of Notch-class genes in humans, modulation of RNA metabolism is likely to be involved in Notch signaling regulation . Indeed,NOTCH1 . YTHDF2,NOTCH1 . HoweverNOTCH1 . In our IGF2BP3, recently identified as a novel m6A reader, has been found elevated in various cancers and involved in the tumorigenesis and tumor progression . PreviouTo sum up, our study revealed that IGF2BP3 acts as an oncogene to promote NPC metastasis by maintaining NOTCH3 mRNA stability via suppressing CCR4-NOT complex-mediated deadenylation, thus leading to constant activation of the Notch3 signaling pathway and enhancing tumor metastasis. Collectively, our findings highlighted an important role of IGF2BP3 in NPC metastasis and elucidated the precise mechanism of IGF2BP3-mediated post-transcriptional regulation on NOTCH3, identifying IGF2BP3 as a novel prognostic biomarker and potential therapeutic target in NPC metastasis.2. All cell lines were checked for no mycoplasma contamination and authenticated using short tandem repeat (STR) profiling.The State Key Laboratory of Oncology in South China  provided the NP69 cell line  and all the NPC cell lines. The NP69 cells were cultured in keratinocyte serum‐free medium supplemented with bovine pituitary extract. NPC cell lines, including C666-1, HNE1, HONE1, HK1, and SUNE1, were grown in RPMI 1640  medium supplemented with 10% FBS  and 1% penicillin/streptomycin . All cell lines were maintained at 37 °C with 5% COWe collected 183 paraffin-embedded samples from patients who were diagnosed clinically and pathologically NPC between 2012 and 2016 at the Sun Yat-Sen University Cancer Center, and subjected them to immunohistochemistry analysis. The clinical information for these samples is shown in Supplementary Table Total RNA was isolated from cells using Trizol . Total RNA (1 ug) was reverse transcribed into cDNA using a GoScript Reverse Transcription System  in a 20 μl reaction mixture. cDNA was detected using a Bio-Rad CFX qRT-qPCR detection system , and quantified using SYBR Green Master Mix . All data were normalized to the expression of housekeeping gene GAPDH.One hundred and eighty-three NPC tissues fixed in 10% formalin and embedded in paraffin were cut into 4 μm sections and placed on slides. The slides were incubated with anti-IGF2BP3  and anti-NOTCH3 antibodies  at 4 °C overnight. The IHC staining results were evaluated and scored by two independent pathologists who were blinded to the clinical outcome. The staining intensity was graded as strong +3, moderate +2, weak +1, and negative 0. Specimens with scores +3 and +2 were defined as high expression, while those that scored as +1 or 0 were defined as low expression. Staining of N3ICD was defined as nuclear NOTCH3 expression in the specimens.6) were injected intravenously into the tail veins of nude mice. The growth of metastatic foci was detected using an in vivo imaging system (IVIS). The mice were sacrificed after 6 weeks and their lungs were removed. Samples were paraffin-embedded and subjected to H&E and IHC staining.Male BALB/c-nude mice (4–5 weeks old) were housed in barrier facilities on a 12-h light/dark cycle and randomly divided into the indicated groups (8 mice/group). All animal experiments were approved by the Ethics Committee of Sun Yat-sen University Cancer Center (L102042021000K). For the lung metastasis model, SUNE1 cells .For the limiting dilution assays, 1 × 10No sample size calculations were performed. Sample size was determined according to our experience as well as literature reporting in terms of specific experiment. Randomization method was used to determine how animals were allocated to different groups. To achieve randomization, all animals were numbered by body weight, then, random number table was used to allocate animals to experimental groups. During the study, no data was excluded from the experiments, and no blinding was done.NPC cells were cultured in 6-well plates overnight, and then treated with 5 µg/ml actinomycin D (MedChemExpress) to inhibit gene transcription for various times. Next, RNA was extracted and analyzed using qRT‐PCR .7 cells were required for each RIP procedure. In brief, NPC cells were lysed in complete RIP lysis buffer containing magnetic beads conjugated with anti-m6A , anti-IGF2BP3 , anti-CCR4  or anti-CAF1  at 4 °C overnight. The proteins were digested with proteinase K after the magnetic beads were washed. The purified RNA was used for qRT-PCR analysis.The RIP assay was carried out using a Magna RIP RNA-Binding Protein Immunoprecipitation Kit , according to the manufacturer’s instructions. 1 × 10http://bigd.big.ac.cn/gsa-human).RNA extraction and RNA sequencing were performed on 5 of normal nasopharynx and 13 of NPC tissues (one of them was a patient-derived xenograft). Differential expression analysis was performed using the DESeq2 software package, and all gene expression values were log2 transformed. The RNA-seq data have been deposited in the Genome Sequence Archive of the BIG Data Center at the Beijing Institute of Genomics, Chinese Academy of Science, under accession number HRA004724  and GraphPad Prism 7 version  were used for the statistical analyses. Log-rank, Chi-squared, One-way ANOVA, and Student’s two-tailed More materials and methods are included in the Supplementary files .Supplementary informationUncropped gel imagesSource data


Myopa tessellatipennis . The genome sequence is 249.3 megabases in span. Most of the assembly is scaffolded into four chromosomal pseudomolecules, including the assembled X sex chromosome. The mitochondrial genome has also been assembled and is 18.3 kilobases in length.We present a genome assembly from an individual female Myopa;Myopa tessellatipennis  (NCBI:txid2829445).Eukaryota; Metazoa; Ecdysozoa; Arthropoda; Hexapoda; Insecta; Pterygota; Neoptera; Endopterygota; Diptera; Brachycera; Muscomorpha; Conopoidea; Conopidae; Myopinae;Myopa tessellatipennis Motschulsky, 1859  is a member of the family commonly referred to as ‘thick-headed flies’, or more recently as ‘bee-grabbers’. As far as is known, all members of this family develop as endoparasitoids of other insects, although life history data for many species is lacking. Hosts primarily comprise species of aculeate Hymenoptera, except in the large and mainly tropical subfamily Stylogasterinae . Elsewhere it occurs throughout the Western Palaearctic.The genusibution .Myopa tBritain , withteication . The Briication  or (Smiication .Myopa tMyopa species but where known comprises bees of the genusAndrena (Hymenoptera: Andrenidae).Myopa pellucida Robineau-Desvoidy, 1830 identified by DNA barcoding from a larva found withinAndrena nitida Müller, 1776, but most of the host-associations in the literature are tentative  was collected from Yeovil, Somerset, England, UK  on 26 April 2021. The specimen was taken from a rural garden by Mike Ashworth (independent researcher) using an aerial net. The specimen was identified by Mike Ashworth and preserved at –80°C. This specimen was used for PacBio and Hi-C analysis.A femaleA male specimen (idMyoTess2) was collected from Wytham Farm, Oxfordshire  on 19 April 2021 by netting. The specimen was collected and identified by Steven Falk (independent researcher), and was preserved on dry ice. This specimen was used for RNA sequencing.DNA was extracted at the Tree of Life laboratory, Wellcome Sanger Institute (WSI). The idMyoTess1 sample was weighed and dissected on dry ice with tissue set aside for Hi-C sequencing. Abdomen tissue was cryogenically disrupted to a fine powder using a Covaris cryoPREP Automated Dry Pulveriser, receiving multiple impacts. High molecular weight (HMW) DNA was extracted using the Qiagen MagAttract HMW DNA extraction kit. HMW DNA was sheared into an average fragment size of 12–20 kb in a Megaruptor 3 system with speed setting 30. Sheared DNA was purified by solid-phase reversible immobilisation using AMPure PB beads with a 1.8X ratio of beads to sample to remove the shorter fragments and concentrate the DNA sample. The concentration of the sheared and purified DNA was assessed using a Nanodrop spectrophotometer and Qubit Fluorometer and Qubit dsDNA High Sensitivity Assay kit. Fragment size distribution was evaluated by running the sample on the FemtoPulse system.RNA was extracted from idMyoTess2 in the Tree of Life Laboratory at the WSI using TRIzol, according to the manufacturer’s instructions. RNA was then eluted in 50 μl RNAse-free water and its concentration assessed using a Nanodrop spectrophotometer and Qubit Fluorometer using the Qubit RNA Broad-Range (BR) Assay kit. Analysis of the integrity of the RNA was done using Agilent RNA 6000 Pico Kit and Eukaryotic Total RNA assay.Pacific Biosciences HiFi circular consensus and 10X Genomics read cloud DNA sequencing libraries were constructed according to the manufacturers’ instructions. Poly(A) RNA-Seq libraries were constructed using the NEB Ultra II RNA Library Prep kit. DNA and RNA sequencing was performed by the Scientific Operations core at the WSI on Pacific Biosciences SEQUEL II (HiFi) and Illumina NovaSeq 6000 (RNA-Seq) instruments. Hi-C data were also generated from head tissue of idMyoTess1 using the Arima v2 kit and sequenced on the Illumina NovaSeq 6000 instrument.Assembly was carried out with Hifiasm . The mitDarwin Tree of Life Project Sampling Code of Practice. By agreeing with and signing up to the Sampling Code of Practice, the Darwin Tree of Life Partner agrees they will meet the legal and ethical requirements and standards set out within this document in respect of all samples acquired for, and supplied to, the Darwin Tree of Life Project. All efforts are undertaken to minimise the suffering of animals used for sequencing. Each transfer of samples is further undertaken according to a Research Collaboration Agreement or Material Transfer Agreement entered into by the Darwin Tree of Life Partner, Genome Research Limited (operating as the Wellcome Sanger Institute), and in some circumstances other Darwin Tree of Life collaborators.The materials that have contributed to this genome note have been supplied by a Darwin Tree of Life Partner. The submission of materials by a Darwin Tree of Life Partner is subject to the What fun! - this is a fascinating understudied parasitic family,  and I hope that this high quality reference sequence will provide one new pathway to learning more about these species. As always the Darwin tree of life project has generated an excellent genome reference that will stand the test of time, and with the other genomes be a foundation of biological research in the 21st century and beyond. I am also impressed and pleased that the DToL is collaborating with entomologists beyond the usual research networks given the importance of this project for all humanity in future years.  In terms of bioinformatic methods, they are well documented, but it would be nice if the assembly pipeline was made public on the various public commercial cloud services - a request for the larger project and beyond the scope of this publication. Another nice touch is that I had no issues finding the reference sequence in the NCBI and EBI public databases. Once again I thank the project for making all the data publicly available as rapidly as possible.  The authors and project as a whole are to be commended on generating a wonderful reference for the community.Are sufficient details of methods and materials provided to allow replication by others?PartlyIs the rationale for creating the dataset(s) clearly described?YesAre the datasets clearly presented in a useable and accessible format?YesAre the protocols appropriate and is the work technically sound?YesReviewer Expertise:Drosophila Genetics, Human Genetics, Arthropod Genomics, Earth BioGenome ProjectI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Myopa tessellatipennis. Both chromosome and mitogenome assemblies appear as of high quality. I have only following minor comment.The first genomic data are presented for the relatively rare flyMyopa tessellatipennis Motschulsky, 1859 as stated in the Background chapter but notMyopa tessellatipennis  as is used in the title and elsewhere in the text. Please correct. Full taxonomic name of the species is It is not clear from the text if the entire body of the specimen was used for DNA or RNA isolation. This should be specified in detail. I strongly recommend to keep at least one wing and male or female terminalia as vouchers for further study and not to destroy the entire abdomen (or body). It is also useful to take (before DNA/RNA isolation) detailed photographs of all important morphological characters used for the taxonomic identification of the specimen(s) and include them in the paper. Habitus photo of mating specimens may be useful to attract attention of potential readers but not to substantiate exact identification of the species. Why a male from different locality was used for RNA isolation and not the male from the mating couple on the photograph? Please provide more details about that. Why RNA was isolated? Are transcriptomic data available? This should be commented on in the text.Are sufficient details of methods and materials provided to allow replication by others?PartlyIs the rationale for creating the dataset(s) clearly described?YesAre the datasets clearly presented in a useable and accessible format?YesAre the protocols appropriate and is the work technically sound?YesReviewer Expertise:Diptera taxonomy; molecular phylogenetics; DNA barcodingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.