Protobothrops mucrosquamatus (Pmu) are frequent in Taiwan, and its wide-spread distribution and diverse habitats drove us to investigate its envenoming effects and relevant venom variations. We used reversed-phase high-performance liquid chromatography and mass spectrometry to analyze 163 Pmu venom samples collected from northern and southeastern Taiwan. Twenty-two major protein fractions were separated and analyzed, and their contents were determined semi-quantitatively. The results showed that despite the trivial differences in the protein family, there is an existing variation in acidic phospholipases A2s, serine proteinases, metalloproteinases, C-type lectin-like proteins, and other less abundant components in the Pmu venoms. Moreover, clinical manifestations of 209 Pmu envenomed patients hospitalized in northern or southeastern Taiwan revealed significant differences in local symptoms, such as ecchymosis and blistering. The mechanism of these local effects and possibly relevant venom components were examined. Further analysis showed that certain venom components with inter-population variation might work alone or synergistically with others to aggravate the local effects. Therefore, our findings of the venom variation may help one to improve antivenom production and better understand and manage Pmu bites.Reports of bite from  Protobothrops mucrosquamatus (Pmu) is a pit viper widely distributed in southern and southeastern Asia, including north-eastern India, Myanmar, northern Thailand, Laos, Vietnam, southern China, Taiwan, and Japan (Ryukyu islands) [Pmu as the most medically important venomous snake in Taiwan and China [Pmu bites result in local tissue swelling; pain; ecchymosis; blistering (bullae formation); wound necrosis; compartment syndrome; and systemic effects, such as thrombocytopenia, coagulopathy, rhabdomyolysis, and renal impairment [islands) . The Wornd China . In addind China . As for pairment . HoweverPmu venom is predominately snake venom metalloproteinases (SVMPs) and phospholipases A2 (PLA2s), followed by snake venom C-type lectin-like proteins (Snaclecs), snake venom serine proteinases (SVSPs), cysteine-rich secretory proteins (CRISPs), and L-amino acid oxidases (LAOs) [The composition of s (LAOs) ,6. Sinces (LAOs) ,8,9. Ints (LAOs) ,11.Pmu bites were conducted by two medical centers in Northern and Southeastern Taiwan, respectively [Pmu, inhabiting these two biogeographical regions can be considered as two distinct populations. In previous studies, a Taiwanese pit viper, Trimeresurus stejnegeri stejnegeri, was found to show inter-population venom variations, such as PLA2 variation, in the two regions [Pmu venom.Recently, two retrospective studies related to clinical manifestations of ectively ,13. Nort regions ,15. ThesPmu remains little understood [Pmu venom components between northern and southeastern Taiwan. Secondly, we examined the clinical manifestations of Pmu envenoming between these two regions. We believe that this study can further assist venom and antivenom research and improve the management of Pmu bites in Taiwan.However, the venom variation of derstood , and itsPmu venoms is shown in Pmu venoms in the reverse phase high-performance liquid chromatography (RP-HPLC) profiles , phospholipase B (PLB), glutaminyl-peptide cyclotransferase (QPCT), and LAO. The major venom toxins in each fraction are listed in Pmu venoms is shown in Pmu venom contained abundant SVMPs (45.1%) and PLA2s (25.0%), followed by Snaclecs (12.8%) and SVSPs (8.23%); however, it contained low contents of 5’-NT (3.85%), LAO (2.37%), CRISP (1.42%), PLB (0.77%), and QPCT (0.46%). These results support the finding of Aird, S. D et al. on Ryukyu Pmu venom proteomics, genomcis, and transcriptomes [Pmu venom from Taiwan [The individual variation in venom components of 163 adult profiles A–C. ShotPmu venoms (119 patients in northern and 44 in southeastern Taiwan) was compared in 2s (25.56% vs. 23.48%), Snaclec (13.15% vs. 11.86%), SVSP (8.26% vs. 8.15%), and other minor protein families. Even though the difference in the average abundance of venom protein families between the two regions was trivial compared to other pit viper species  eluted from HPLC , which did not show variation, acidic PLA2s (Fr 4 and 6) presented a significant variation  [Pmu population  was only present in low amounts in some Pmu venom from both regions    was signon Fr 6, A. Furthens Fr 4, A. Notabl< 0.001) . However samples ,19. Therresearch , we did Pmu venom samples   in the northern region  was identified in Fr 7. Its relative abundance  was consistently present in venoms from both ulations , and itsulations B. On thebundance A and pro2 variants , Snaclec, and metalloproteases of PII-class by analyzing many individual Pmu venoms  and blistering . No significant difference was found for local complications, such as tissue necrosis  and compartment syndrome , or in the systemic complications. There were slightly higher proportions of cellulitis  and renal impairment  in patients from the northern region than those from southeastern area.A total of 209 stations . A higheCrotalus pyrrhus bites in the western and eastern regions of Arizona state in America, respectively [Pmu bites.Currently, there are only a few studies that examined the difference in snake envenoming effects regarding the geographic locations available, with most of them focusing on the comparison of the severity score and antivenom dosage following bites between regions ,8,9. OurPmu envenoming of northern victims  in vytotoxic  and possytotoxic . Based o2 induces cell membrane damage through a non-catalytic mechanism and release inflammatory mediators in tissues [2s enhance the release of inflammatory mediators synergistically with K49-PLA2 [2 in the northern Pmu venom may jointly attribute to more severe local symptoms. Therefore, this information could be important for antivenom manufacturers and clinicians in assessing antivenom efficacy and effectiveness in managing Pmu bites. Although the differences of minor components  in venoms were found between regions, their clinical significance in Pmu envenoming remains undetermined [Venom-induced blistering, characterized by the separation of the epidermis and dermis , is a cl tissues ,40,41,42K49-PLA2 ,30. The termined ,44,45,46Pmu venoms and clinical envenoming effects in different regions in Taiwan has strong potential in improving therapeutic implications in snakebite management.Although we found inter-population variations in venom compositions and suggested the associations of venom variations with clinical manifestations, this study has some limitations. First, the body size, age, and sex of individual snakes; the amount of venom injection; and the health status of patients, which are important factors that may have affected the severity and treatment of envenomation, are difficult to determine in such retrospective studies. Second, it is challenging to separate and analyze snake venom components by RP-HPLC with gel electrophoresis because of their similar molecular weights or hydrophobicity. Furthermore, the method used for protein resolution or separation (mainly re-dissolved or reconstituted venom solution) might cause certain non-protein loss and precipitation of high-molecular-weight constituents, making these components undetectable. Third, the bottom-up venomomics applied in this study may provide incomplete compositional information of venoms, especially for low-abundance components. The top-down mass venomics may eliminate the shortcomings of bottom-up workflows ,48. FinaPmu venom protein fractions 4, 7, 8, 10, 11, 13, 14, 17, 18, and 19 . A higher proportion in fractions 7, 11, 17, 18, and 19  was noticed in the northern snakes’ population. Additionally, the amount of relevant protein families is higher in fractions 3, 6, 7, 8, 10, 13, 14, 15, 17, 18, 20, and 22 in the northern snakes’ population even though the presence of certain fractions  does not differ between regions.We found that there is inter-population variability in the presence of 2 in the Pmu venoms obtained from the same areas. Our study provides objective information on Pmu venoms, and these findings can have therapeutic implications for future antivenom manufacturing processes, efficacy evaluation against these major fractions, and snakebite management in Taiwan.Furthermore, we found a higher risk of developing ecchymosis and blistering in the northern patients’ population, which is suggested to be associated with the higher abundance of PI-SVMP (trimerelysin-2 like), PII-SVMP (PMMP-3), Snaclec (Trimecetin), SVSP (TSV-PA like), and acidic E6-PLAPmu were obtained from two regions: Taipei Basin and the surrounding mountain areas in northern Taiwan (n = 119) and East Rift Valley in southeastern Taiwan (n = 44)  . Each inPmu crude venom was reconstituted in ultrapure water containing 0.1% trifluoroacetic acid  and 5% acetonitrile  and centrifuged at 10,000× g for 10 min. The protein concentration of the supernatant was determined using the Pierce bicinchoninic acid (BCA) protein assay kit . Venom proteins were separated by RP-HPLC following the method of Villalta, M. et al. study [Individual Pmu001 , Pmu002 (collected from Taipei Basin), and Pmu003 (collected from Taipei Basin). Pmu003, containing the most resolvable fractions, was chosen as the primary reference  were boiled for 5 min and alkylated with 135 mM iodoacetamide . Then, venom proteins were separated through SDS-PAGE, and the gel was stained with Coomassie brilliant blue. Further preparation of MS samples was conducted by cutting each stained band into a few slices, which were destained in 10% methanol , dehydrated in acetonitrile, and dried in a SpeedVac. Proteins in the gel were digested by sequencing-grade modified porcine trypsin  overnight at 37 °C. Then, the tryptic peptides were extracted with 200 μL of 0.1% formic acid  and 50% acetonitrile solution, with 70% acetonitrile in ultrapure water, dried in SpeedVac, and finally stored at −80 °C for subsequent processing.3)2O)6H+ at 445.120025 (m/z) was used as a lock mass for internal calibration.Peptide mixtures were reconstituted with 0.1% formic acid and analyzed by LC-MS/MS using an LTQ Orbitrap Velos mass spectrometer coupled with a nano-LC . Further, the sample was loaded into a trap column  at a flow rate of 0.2 μL/min in solution A (0.1% formic acid in ultrapure water) and separated on a resolving 10-cm analytical C18 column  using a 15-μm tip . The peptides were eluted using a linear gradient of 0–10% solution B (0.1% formic acid in acetonitrile) for 3 min, 10–30% solution B for 35 min, 30–35% solution B for 4 min, 35–50% solution B for 1 min, 50–95% solution B for 1 min, and 95% solution B for 8 min, with a flow rate of 0.25 μL/min. The Orbitrap’s resolution was set at 30,000, and the ion signal of  and searched against a non-redundant Swiss-Prot database of Serpents  using MASCOT. Search parameters for tryptic peptides included one missed cleavage, a mass tolerance of 0.5 Da for fragment ions, fixed cysteine modification with carbamidomethylation, variable methionine oxidation, variable N-terminal acetylation, and variable transformation of N-terminal glutamate or glutamine to pyroglutamic acid. Finally, the criteria used for protein identification were high peptide confidence; the lowest peptide length was five amino acids, and the minimal number of peptides per protein was two.For MS scan, the The relative content of each major protein family was estimated as described by Calvete et al. . Then, tThis is a secondary analysis of the retrospective cohort studies ,13. All Pmu envenomation were classified into three categories: (1) local symptoms, including ecchymosis (9/L), coagulopathy , renal impairment (with serum creatinine level >1.4 mg/dL), and rhabdomyolysis (with blood creatine kinase level >1000 U/L). The study protocols were approved by the Institutional Review Board of Taipei Veterans General Hospital (95-02-25A) and Hualien Tzu Chi Hospital (IRB102-38).The clinical data collected included patients’ clinical presentation, laboratory analyses, clinical outcomes, and complications. The clinical manifestations of chymosis A,B and bchymosis B,C; (2) chymosis D, and cot-test and presented as mean ± standard deviation (SD); non-normal variables were compared by the Mann–Whitney U test reported as the median interquartile range (IQR). Categorical variables were compared using the Chi-square test or Fisher’s exact test. We analyzed all the data using the Statistical Package for the Social Sciences v.21.0 . A two-tailed p-value of <0.05 was considered statistically significant.The normality of distribution of continuous variables was tested by one-sample Kolmogorov–Smirnov test. Continuous variables with normal distribution were compared using independent samples Student’s


In the present study, adult mice were used to assess the impact of 2,6-DCBQ via measuring the responses of antioxidant enzymes (superoxide dismutase (SOD) and catalase (CAT)), the key genes (Heme oxygenase-1 (HO-1), NADPH quinone oxidoreductase 1 (NQO1) and glutamate-L-cysteine ligase catalytic subunit (GCLC)) in the Nrf2-keap1 pathway, and lipid peroxidation . Our results clearly indicated that 2,6-DCBQ decreased the activities of SOD and CAT, repressed the transcriptional levels of key genes in Nrf2-keap1 pathway, further caused oxidative damage on mice. These results provided evidence for assessing the threat of 2,6-DCBQ on human. Disinfection byproducts (DBPs) are a kind of compounds unintentionally produced from the reactions of natural organic matter (NOM) with disinfectants during the water disinfection process ,2,3. HitE. coli cells [Up to now, 12 kinds of HBQs were identified in the drinking waters, of them, 2,6-DCBQ was proved to be the most toxic and highest levels DBPs ,11, and li cells , our preli cells . Most ofUnder normal circumstances, there is a balance between oxidative stress and antioxidant system in organisms. While xenobiotic induce organism generate more reactive oxygen species (ROS), meanwhile, the organism correspondingly improved antioxidant capacity to counteract the ROS ,16. SupeAlthough previous studies provided some evidence regarding as the toxic effects of 2,6-DCBQ, those results aren’t suitable for human due to higher species difference. Due to sharing similar morphologic structure and the cellular composition with human, mouse is considered as a reliable research model for toxicology research ,23. Give3(CH2)3COCH(CH3)2, CAS: 13019-20-0, purity ≥ 98%) was purchased from ThermoFisher Scientific Co., Ltd. . Ethanol solution  was used to make 10 g L−1 stock solution and frozen at −80 °C before use. Different concentrations of test solutions were prepared by dissolving 2,6-DCBQ stock solution into 0.9% saline solution. The reagent kits used in biochemical, and assays were purchased from Nanjing Jiancheng Bioengineering Research Institute . The primers, reagents and kits for gene expression measurement were purchased from Sangon Biotech Co., Ltd. , and TaKaRa Co., Ltd. .2,6-DCBQ , fifty ICR female mice  were acclimated to the standard animal house. All mice were cared following the method reported in our previous studies . Brieflyw/v), and then stored in a −80 °C refrigerator for following biochemical assays.Different concentrations of 2,6-DCBQ were prepared according to Shi et al. study , they ha−1 protein), SOD (U mg−1 protein), and MDA (U mg−1 protein) were determined using the Diagnostic Reagent Kit . Furthermore, the protein level of each sample was also measured using the Kit (Coomassie protein assay dye) to quantitate the activities of CAT and SOD, and MDA levels.The impacts of 2,6-DCBQ on oxidative stress and damage were determined following the method described in Zhang, et al. . Brieflyn = 3) was conducted using Trizol according to the method described in our previous studies [2O. The thermal cycling program was set as follows: initial denaturing at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s, and 60 °C for 30 s. Three biological replicated experiments. Three biological replicated experiments, and the gene expressions of these key genes were calculated and normalized against the housekeeping gene β-actin using the equation fold change = 2−∆∆T [To understand the response of mouse to 2,6-DCBQ-induced ROS, several key genes  involved in Nrf2-keap1 pathway were evaluated at molecular levels. Total RNA extraction from livers of mice from each group . Also, error bars were revealed by standard deviation (mean ± SD). All data are presented as mean ± SD. The data on the activities of CAT and SOD and the contents of MDA were evaluated by one-way analysis of variance followed by Duncan’s multiple range test (a = 0.05). All figures and statistical analyses were carried out with Sigmaplot 11.0.Although 30 days exposure of 2,6-DCBQ had no significant effect on the mice growth , the preThere is a dynamic balance between ROS and antioxidant system in organism, whereas 2,6-DCBQ exposure induced more ROS generation and disrupted the balances. Meanwhile, the organism improved antioxidant capacity to deal with excessive ROS. SOD and CAT, as the high-efficiency antioxidant enzymes, constitute the major defensive system against oxidative stress . As showCollectively, 2,6-DCBQ exposure caused excessive ROS generation. ROS is active, which pose a potential threat to human health; therefore, it is urgent to further evaluate the effect of ROS caused by 2,6-DCBQ.Nrf2-Keap1 pathway can counteract ROS by controlling some antioxidant genes, which play a crucial role in protecting cells from oxidative stress. Once oxidative stress occurred, Nrf2 is activated from keap1 . In the In short, 2,6-DCBQ exposure not only decreased the activities of SOD and CAT but also caused the reduction to Nrf2 and Keap1 mRNA transcriptional levels. In order to alleviate oxidative stress, more antioxidant elements were involved in antioxidant reaction, we will further explore the response of key genes in Nrf2-keap1 pathway to understand the impact of 2,6-DCBQ on mice.Heme oxygenase-1 (HO-1), NADPH quinone oxidoreductase 1 (NQO1) and glutamate-L-cysteine ligase catalytic subunit (GCLC), as the downstream genes in Nrf2-Keap1 pathway, play a crucial role in the cellular defense against oxidative stress in organism ,30,32. OFor NQO1, it is an endogenous molecule with a conjugated double bond system, which is responsible for maintaining the redox homeostasis in organism . In the For GCLC, it is the rate-limiting enzyme for GSH synthesis, and GCLC has been considered as the indicator to indirectly reflect the degree of oxidative damage. In this experiment, GCLC mRNA transcription displayed similar variation to that of HO-1 and NQO1 . This prIn short, HO-1, NQO1 and GCLC mRNA transcriptional levels were inhibited by 2,6-DCBQ. These results indicated that oxidative stress caused by 2,6-DCBQ far exceeded the capacity of antioxidant defense, and probably further induce oxidative damage on the mice. In order to understand the impact of 2,6-DCBQ, further assessment was conducted.−1 2,6-DCBQ exerted higher toxic effect, decreasing the total polyunsaturated fatty acids which was prone to peroxidative damage. Similar case was also shown in our previous study, we found that higher concentrations of microcystin-LR decreased MDA levels [Excessive ROS can cause a series of detriment effects on organisms, of these, MDA is considered the most direct outcome of oxidative damage . In the A levels . This reTaken together, 2,6-DCBQ exposure induced severe oxidative damage, and ≥10 mg/kg oral exposure posed a considerable threat to mice.Our results clearly indicated that, after exposure to 30 days, 2,6-DCBQ significantly decreased the activities of SOD and CAT, inhibited the transcriptional levels of key genes  in Nrf2-keap1 pathway. Besides, 2,6-DCBQ also caused oxidative damage to the mice. This study provides novel insight into 2,6-DCBQ-induced toxicity, more work is needed to elucidate the mechanisms for 2,6-BDCQ-induced toxicity.


A. sinensis were reported. According to the phylogenetic analysis, they were classified as nege-like virus, iflavirus, chu-like virus, and ollusvirus. The complete genome sequence of four RNA viruses were obtained with RACE. Moreover, the analysis of virus-derived small interfering RNAs showed that most of the RNA viruses were targeted by the host antiviral RNA interference pathway.The present work focused on the RNA virome analysis of an agricultural pest. In this study, the four novel RNA viruses in Atractomorpha sinensis (Orthoptera: Pyrgomorphidae), an important agricultural pest known as the pink-winged grasshopper. These insect viruses were classified as the nege-like virus, iflavirus, ollusvirus, and chu-like virus using HTS and phylogenetic analyses. Meanwhile, the full sequences of four novel RNA viruses were obtained with RACE and named Atractomorpha sinensis nege-like virus 1 (ASNV1), Atractomorpha sinensis iflavirus 1 (ASIV1), Atractomorpha sinensis ollusvirus 1 (ASOV1), and Atractomorpha sinensis chu-like virus 1 (ASCV1), respectively. Moreover, the analysis of virus-derived small interfering RNAs showed that most of the RNA viruses were targeted by the host antiviral RNA interference pathway. Moreover, our results provide a comprehensive analysis on the RNA virome of A. sinensis.A large number of RNA viruses have been discovered in most insect orders using high-throughput sequencing (HTS) and advanced bioinformatics methods. In this study, an RNA virome of the grasshopper was systematically identified in  With the aid of large-scale metatranscriptomic sequencing, an increasing number of novel RNA viruses have been discovered in insects, which can broaden our horizons of RNA viruses’ diversity ,2. TheseHematophagous and sap-sucking insects play an important role in the transmission of arthropod-borne viruses (arboviruses). Previous studies have shown that several IRVs isolated from hematophagous insects interact with arboviruses and affect vector competence for arboviruses. For example, IRVs in the Flaviviridae modulate arboviruses’ replication and transmission in mosquitoes and sandflies ,7,8. NegAtractomorpha sinensis, commonly known as the pink-winged grasshopper (Orthoptera: Pyrgomorphidae) [A. sinensis has not attracted wide attention to its outbreak and potential damage over the past several decades.rphidae) ,18, is arphidae) . In termrphidae) ,20. NeveA. sinensis. As revealed through analysis on sRNA data, RNAi-mediated antiviral immunity was active during the infection of these RNA viruses.In this study, four novel RNA viruses were identified in A single adult pink-winged grasshopper was collected in the field of Huzhou, Zhejiang, China, in September 2019. This sample was delivered to our laboratory and stored at −80 °C until further analysis. Then, insect morphology was identified under a dissecting microscope and verified with Sanger sequencing using a pair of universal primers to amplify mitochondrial cytochrome c oxidase subunit 1 (COI) genes.The single sample was homogenized in liquid nitrogen, and total RNA with rRNA was extracted using TRIzol reagent  following the manufacturer’s instructions. High throughput sequencing (HTS) and small RNA sequencing were conducted after evaluating the quality of total RNA with rRNA through Nanodrop . The Illumina TruSeq Total RNA with rRNA Sample Preparation Kit was adopted to construct the cDNA library. A small RNA library was established using the Truseq Small RNA Library Preparation Kit  and sequenced based on the Illumina HiSeq 2500 platform, which resulted in 19 Mbp of raw data. Furthermore, raw RNA reads were subject to quality control, adapter trimming, and contig assembly using Trinity program (Version 2.8.5)  with def−20 for each comparison. Based on blast hit results, unassembled overlaps and same viral reads were merged by using SeqMan program of DNAstar software package to form partial/complete virus genomes. All candidate viral reads were verified through RT-PCR following Sanger sequencing. The first strand of complementary DNA (cDNA) from 1000 ng of total RNA was synthesized with HiScript ®II Q RT SuperMix for PCR (+gDNA wiper)  in line with the manufacturer’s protocol. RT-PCR was performed in 10 µL-reaction agents containing 0.5 µL template cDNA, 5 µL of 2 × Phanta Max Buffer, 0.2 µL dNTP mix, 0.2 µL of Phanta Max Super-Fidelity DNA Polymerase , 0.2 µL of 1 µM forward and reverse primers, and 3.7 µL of ddH2O. The thermal cycling conditions were 95 °C for 5 min, 40 cycles of 95 °C for 30 s, 60 °C for 30 s, and 70 °C for 30 s. Abundance of each virus genome was estimated by mapping quality-checked raw reads to virus genomes with Bowtie 2 and Samtools programs [To identify viral reads, the assembled RNA-seq contigs were queried against the non-redundant nucleotide (nt) and non-redundant protein (nr) sequence databases in NCBI using Blastn and Blastx algorithm, respectively. Furthermore, the candidate viral reads were compared against the Viral RefSeq database downloaded from GenBank in order to avoid false positive reads. E-value cut-off was set at 2 × 10programs ,23. Thenprograms .® RACE 5′/3′ Kit . Both 5′ and 3′ PCR products were amplified with sequence-specific primers with nest-PCR and cloned into a pMD19-T clone vector individually  after Sanger sequencing, to obtain the complete virus genome sequence. The primers used for identification of virus-like sequences are listed in To obtain the full-length genome of the candidate virus, the rapid amplification of cDNA ends (RACE) assay was performed as described . BrieflyA. sinensis, the clean small RNA (sRNA) reads (18- to 30-nt) were mapped back to full-length virus genome sequences using Bowtie 2 with zero mismatches. In addition to that, virus-derived siRNAs were further vegetated using the Linux bash scripts and the custom perl scripts.To explore the small interfering RNA (siRNA) triggered by viruses in https://www.ncbi.nlm.nih.gov/cdd/, accessed on 5 March 2022) and InterProScan Search .The potential open reading frames (ORFs) in the virus genomes were predicted using the ORF Finder program in NCBI. The function annotation of each ORF was conducted through the NCBI Conserved Domain Database  were generated from the obtained cDNA library, which had deep sequencing on the Illumina HiSeq 4000 platform (150-bp paired-end reads) of Novogene. In total, 86,784,590 reads were generated and assembled de novo into 464,844 reads in the library. Through a Blastn search against the nucleotide database in NCBI, it was found that the insect COI sequence showed a high homology (99% identities) to that of A. sinensis (accession number: MW085548.1). In total, 17,356 virus-like reads were found in the library. After a BLASTn search against the NCBI nucleotide (nt) and the entire viral reference database, four novel RNA viruses were identified in the assembled contigs of A. sinensis, which were classified as nege-like virus, iflavirus, ollusvirus, and chu-like virus, respectively. The sequences of four viruses were uploaded to NCBI, and the accession numbers include OL672484, OL672485, OL672486, and OL672487. It was shown that three reads were almost identical to those of the Aphid lethal paralysis virus (ALPV), suggesting that these reads were derived from a strain of ALPV  was 12,278 nt (excluding polyA tail), which contained a 69 nt 5′ untranslated region (UTR), a 457 nt 3′ UTR, and four predicted ORFs that further included ORF1 , ORF2 , ORF3 , and ORF4 . ORF1 was featured by four conserved domains, namely, Vmethyltransf super family (vMet) domain (253–1089 nt), FtsJ-like methyltransferase (FtsJ) domain (2206–2775 nt), Viral_helicase1 (Hel) domain (5689–6483 nt), and RdRP_2 super family (RdRP) domain (6994–8448 nt) A, whereave sense C.The analysis of the phylogenetic relationship showed that ASNV1 was the cluster with Ganwon-do negev-like virus 1, together with Ingleside virus, Nephila clavipes virus 4, and Hubei virga-like virus 13, forming unclassified group1, showing a close relationship with the Kitaviridae B. AnalysThe sRNA library was constructed to analyze virus-derived small RNA (vsiRNA) of ASNV1; as a result, ASNV1 triggered vsiRNA with peaks of 22 nt related to RNAi-mediated antiviral immunity C. The vsA. sinensis replicated in an efficient way (The full-length sequence of Atractomorpha sinensis iflavirus 1 (ASIV1) contains 9, 412 nt with the 791 nt 5′ UTR, the 118 nt 3′UTR, and a predicted ORF (792–9293 nt), where the ORF contained two rhv_like (rhv) domains (1827–2060 nt and 2631-2954 nt), the CRPV_capsid super family (CRPV) domain (3648–4304 nt), the Viral_helicase1 (Hel) domain (5208–5531 nt), and the RdRP domain (7761–9254 nt) A. Meanwhient way A.Moreover, phylogenetic analysis results demonstrated that ASIV1 displayed a close relationship with another virus in Iflaviridae, especially Hubei coleoptera virus 1 and Tribolium castaneum iflavirus B. FurtheFor vsiRNA, ASIV1 triggered vsiRNA with peaks of 22 nt C, which A. sinensis (The full-length sequences of Atractomorpha sinensis ollusvirus 1 (ASOV1) and Atractomorpha sinensis chu-like virus 1 (ASCV1) possess 14,715 nt and 7673 nt, accordingly. The sequence structures including an 85 nt 5′UTR, a 252 nt 3′UTR and three predicted ORFs were included in ASOV1, where the three predicted ORFs were ORF1 (86-3406 nt) encoding 1107 amino acids, ORF2 (4440-6805 nt) encoding 789 aa, and ORF3  encoding 2475 aa. Meanwhile, the InterProScan was employed to predict the conserved domain of ASOV1. The ORF1 and ORF2 had no conserved domain, while ORF3 possessed three conserved domains, namely, RdRP domain , mRNA_cap domain , and FtsJ-like methyltransferase (FtsJ) domain  A. For ASsinensis A,B.Furthermore, the evolutionary relationship of ASOV1 and ASCV1 was explored with other viruses with phylogenetic analysis. ASOV1 had a close relationship with Culverton virus and Scaldis River bee virus, both of which were classified into the ollusvirus in the Aliusviridae with the aa sequence identity of 28.5–52.2%. ASCV1 clustered with Blattodean chu-related virus, which belonged to an unclassified group (Group 2) of Chuviridae, with 61.3% aa and 64.2% nt sequence identities .ASOV1 triggered the peaks of 22 nt D, and unA. sinensis. Negeviruses have a broad host range as a group of insect-specific viruses, including the orders of Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera, Odonata, Orthoptera, and Thysanoptera [A large number of RNA viruses were discovered using HTS and advanced bioinformatics methods, thus, gaining a better understanding of insect viromes and viral evolution. Recently, numerous RNA viruses have been identified in multiple grasshopper species . In thisanoptera . In the anoptera ,35. Iflaanoptera . In addianoptera ,38. The anoptera . In our A. albopictus C6/36 cell line [A. sinensis responded strongly during the infection of three RNA viruses.As potential biocontrol agents, ISVs have attracted more attention due to their effects on vectoring ability. In addition, they impede some arboviruses’ infections in vivo and in vitro in mosquitos, such as Nhumirim virus and Zika virus, participate in reducing West Nile virus transmission when co-infecting the mosquito vector , and areell line . Otherwiell line ,40,41. IA. sinensis and classified as nege-like virus, iflavirus, ollusvirus, and chu-like virus according to a phylogenetic analysis. Except for ASCV1, other RNA viruses were targeted by the antiviral RNA interference pathway in A. sinensis.In conclusion, ASNV1, ASIV1, ASOV1, and ASCV1 were identified in


ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1) were found to be related to AML patient survival time. We then compared the hub gene expression levels between AML peripheral blood (PB) samples (n = 162) and control healthy whole blood samples (n = 337). Seventeen of the hub genes showed lower expression levels in AML PB samples. The gene expression analysis was also done among AML BM (bone marrow) samples of different stages: diagnosis (n = 142), posttreatment (n = 42), and recurrent (n = 12) stages. The results showed a significant increase of ANXA3, CEACM1, RGL4, RILPL1, and HP in posttreatment samples compared to diagnosis and/or recurrent samples. Transcription factor (TF) prediction of the hub genes suggested LTF as the top hit, overlapping 10 hub genes, while LTF itself is just one of the hub genes. Also, 3671 correlation links were shown between 128 mRNAs and 209 lncRNAs found in survival time-related modules. Generally, we identified candidate mRNA biomarkers based on CN-AML data which can be extensively used in AML prognosis. In addition, we mapped their potential regulatory mechanisms with correlated lncRNAs, providing new insights into potential targets for therapies in AML.The prognosis of acute myeloid leukemia (AML) remains a challenge. In this study, we applied the weighted gene coexpression network analysis (WGCNA) to find survival-specific genes in AML based on 42 adult CN-AML samples from The Cancer Genome Atlas (TCGA) database. Eighteen hub genes ( The malignant hematologic disease, acute myeloid leukemia (AML), is a heterogeneous clonal disorder of myeloid progenitors that accumulates due to a blockage in their differentiation and infiltration into other organs of the body , leading to death –3. The pNPM1 and CEBPA, in which mutations have been proposed as good prognostic factors, as well as PLT3, RUNX1, ASXL1, and TP53, in which mutations have been considered to be correlated with poor prognosis [dimCD117+ phenotypical abnormal cell ratio > 2.055% within 2 weeks after the first complete remission (CR) is considered to be an independent risk factor for recurrence, which also is an adverse factor for relapse-free survival (RFS) and overall survival (OS) in adult AML patients [Cytogenetically normal acute myeloid leukemia (CN-AML) presents without microscopically detectable chromosomal abnormalities and contributes to approximately 50% of the observed AML cases . Heterogrognosis , 5. Trearognosis . Also, iTo better understand the complex prognostic gene expression signatures of CN-AML and investigate potential targeted therapies, we performed the weighted gene coexpression network analysis (WGCNA) on the RNA-seq data of adult patients with CN-AML, available from The Cancer Genome Atlas (TCGA). Our study identified survival-specific genes and provided system-level evidence of genetic networks that contribute to the prognosis of adult CN-AML patients. What is more, the survival-specific genes we found based on CN-AML samples also showed prognostic values in AML samples regardless of any clinical characteristics .https://portal.gdc.cancer.gov/) (project TCGA-LAML [CGA-LAML ) for thehttp://www.ncbi.nlm.nih.gov/geo/; accession number GSE12417 [To perform the survival analysis among the hub genes obtained after the WGCNA, we chose 148 adult (≥18 years) CN-AML patients with an OS > 30 days from the Gene Expression Omnibus (GEO) database  [To compare the expression levels of hub genes and correlatedly expressed lncRNAs in AML BM samples of different stages, we chose 196 samples of 163 patients from an independent cohort in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database (/target) . The samhttps://portal.gdc.cancer.gov/) and 29 samples from the TARGET (https://ocg.cancer.gov/programs/target) database (samples from TCGA database do not include the 42 CN-AML used for WGCNA). The AML PB sample screening criteria were primary AML PB samples of the diagnosis stage with RNA expression profiles; AML PB samples were selected regardless of their clinical characteristics  to serve as normal controls. The healthy whole blood sample screening criteria were healthy whole blood samples with RNA expression profiles.To compare the expression levels of hub genes and correlatedly expressed lncRNAs between primary AML PB samples at the diagnosis stage and normal whole blood samples, we chose 133 samples from TCGA database (We collected the fragments per kilobase of exon model per million (FPKM) mapped reads  and stanβ values for mRNAs, miRNAs, and lncRNAs was 5, 4, and 4, respectively ) was applied to statistically identify enriched pathways and gene ontologies (GO) [Q value < 0.05 [The ToppGene database (ies (GO) . The cute < 0.05 . The rese < 0.05 , 18.https://string-db.org/) was used to construct the PPIs [The online database Search Tool for the Retrieval of Interacting Genes (STRING) (Version 11.0)  [The survival analysis based on the hub gene mRNA expression levels and patient OS was analyzed by an online tool, GenomicScape (pe.com/) . The prohttps://xenabrowser.net/) [The expression matrices with RSEM (RNA-Seq by Expectation Maximization)  normalizer.net/) .https://amp.pharm.mssm.edu/chea3/) website [TF prediction for the hub genes was done via the ChEA3 ( website .http://www.rstudio.com), Microsoft Excel 2007, the Cytoscape software (Version 3.7.0), and GraphPad Prism 7 were used for all statistical analysis or graphic drawings in this research. P values < 0.05 were considered statistically significant [The RStudio software  1 module of mRNAs, as well as ME2, ME3, and ME4 modules of lncRNAs, showed positive associations with the survival time of adult patients with CN-AML Figures , suggestP = 1.2e − 12) and the ME3 module of lncRNAs  and strongly correlated in ME2  and ME4  modules of lncRNAs, indicating that genes significantly associated with survival time were also key elements of modules associated with survival time. Thus, we considered genes from the ME1 module of mRNAs, together with those in the ME2, ME3, and ME4 modules of lncRNAs, as survival-specific in adult patients with CN-AML.To further explore the association of these four modules with patient survival time, we used GS and MM measures to identify the genes with both high GS for “survival time,” as well as high MM in the selected modules. As shown in Figure To explore the survival-specific protein-coding genes, the GO analysis of BP, MF, and CC, as well as pathway analyses, was performed on the 131 mRNAs of the ME1 module. The top 20 GO terms of each category are shown in Next, we established a PPI network of the ME1 mRNAs recognized in STRING, as shown in Figure ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1.We obtained 18 hub genes from 131 mRNAs of the ME1 module by the method we described above, using the cytoHubba plugin of the Cytoscape software. These were C11orf42) had a decreased expression level in AML PB samples compared to healthy samples (P < 0.05).The expression levels of the hub genes in PB samples of primary AML patients from the diagnosis stage and healthy whole blood samples were analyzed. We separately compared TCGA AML PB samples vs. GTEx healthy samples Figure S A and TANXA3 and CEACM1 are significantly upregulated in posttreatment samples compared to diagnosis samples (P < 0.05). RGL4 and RILPL1 are significantly upregulated in posttreatment samples compared to both diagnosis and recurrent samples (P < 0.05). ABCA13, ARG1, CRISP2, and CYP4F3 showed higher expression levels in recurrent samples than in diagnosis samples (P < 0.05). Also, the expression level of HP in posttreatment samples is significantly higher than that in recurrent samples (P < 0.05).Next, we compared the hub gene expression levels in primary AML BM samples of three different stages: diagnosis stage, posttreatment stage, and recurrent stage . From ARG1, CEACAM1, CHI3L1, CRISP2, and CYP4F3, were significantly correlated with a longer OS (P < 0.05)  .Lactotransferrin (LTF) ranked the first place with the lowest mean rank [CEACAM3, CEACAM1, ANXA3, ARG1, CYP4F3, CHI3L1, PADI2, RGL4, MMP8, and ABCA13). Also, LTF is just one of our 18 hub genes.We then predicted TFs for the 18 hub genes by ChEA3 website. The top 10 TFs were listed in ean rank  and the R| > 0.5, P < 0.05). In particular, 127 mRNAs and 28 lncRNAs formed 224 very strong [R| > 0.8 (P < 0.05)  . The top cancers . We anal samples . No sign samples . Since wPatients with AML without chromosomal changes are diagnosed as CN-AML . Having WGCNA, an algorithm for a scale-free network introduced in 2005, has been used to propose candidate therapeutic targets or predict diagnosis, classification, progression, or prognosis in various types of cancers –37. As aAfter constructing a PPI network of 131 mRNAs and mRNA-lncRNA network carried out by Pearson's correlation analysis, we used the cytoHubba plugin of Cytoscape software to find hub genes. CytoHubba provides 12 topological analysis methods, which are MCC, DMNC, MNC, Degree, EPC, BottleNeck, EcCentricity, Closeness, Radiality, Betweenness, Stress, and ClusteringCoefficient, to rank nodes in a network by the network features . These nARG1, CEACAM1, CHI3L1, CRISP2, and CYP4F3 with a longer OS.In expression analyses of the hub genes in different cohorts of AML samples and healthy whole blood samples, 17 of the 18 hub genes showed higher expression levels in AML PB samples than in healthy whole blood samples. Nine genes showed higher expression levels in AML BM samples in the posttreatment stage, compared to the diagnosis and recurrent stages. These results were consistent with our expectation of prognostic values of these genes. And it also proved that these potential biomarkers extracted based on CN-AML sample data may be extensively applicable to all kinds of AML samples, regardless of clinical traits. Also, survival analysis of the 18 hub genes in 148 GEO CN-AML patients showed the correlation of higher expression levels of CEACAM1, CRISP2, and CYP4F3 showed their strong competitiveness in both expression analyses  and the survival analysis. They can be key study genes in our further research. Their relationship with tumor progression has been reported in previous studies. Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) mediates the direct interaction between tumor and immune cells as a cell-cell communication molecule [Cysteine-rich secretory protein-2 (CRISP2) has been reported to be less expressed in high-grade squamous intraepithelial lesions than in other histological grades, making it a novel biomarker for the detection of cervical cancer [Cytochrome P450 family 4 subfamily F member 3 (CYP4F3) has been reported to have good diagnostic values for osteosarcoma [CYP4F3 (rs4646904) may contribute to the etiology of lung cancer [CYP4F3A was upregulated in all-trans-retinoic acid- (ATRA-) treated AML cell line HL-60 [In the 18 hub genes, molecule . It has molecule –57, whill cancer . Cytochrosarcoma , and a pg cancer . Mizukamne HL-60 .LTF  was predicted to be a transcription factor to 10 of the 18 hub genes. It is a member of the transferrin family of genes, and its protein product is found in the secondary granules of neutrophils. Its relationship with various types of tumors including AML has been widely reported. Back in 1988, Davey et al. reported a quantitative decrease in LTF staining in AML and myelodysplasia, which supports the concept that abnormal neutrophils and bands are derived from a malignant clone of myeloid precursor cells [LTF to be a candidate biomarker for AML prognosis.or cells  and alsoDNA methyltransferase 3 alpha (DNMT3A) mutation positive and negative AML samples  [The pathway enrichment analysis suggested innate immune system, neutrophil degranulation, and ensemble of genes encoding ECM-associated proteins  as the top three hits with the hit gene number > 10% of the ME1 mRNAs . The innate immune system has been widely reported to be closely related to various kinds of cancers including AML , 64. Neuomarker) . ECM-assomarker) –68. EMC-omarker) . Berdel omarker) .LINC00671 is one of the lncRNAs revealing a high expression correlation with mRNAs in our PPI network. It was previously found to be a tumor suppressor in multiple cancers including renal cell cancer, pancreatic cancer, and papillary thyroid tumor by inhibiting the growth and metastasis of cancer cells [er cells –29. AlthThere are previous studies investigating AML based on the WGCNA method. Wiggers et al.  identifiAdmittedly, this work was limited by the sample size and statuses of our WGCNA—42 samples (deceased patients only) were included. More comprehensive studies of larger sample sizes should be performed in the future. Additionally, our study was a bioinformatics analysis. The mRNAs and their potential regulatory lncRNAs identified in this study for their prognostic values should be further investigated by in-depth mechanical approaches such as RT-PCR validation and gene function experiments. To use these results in clinical prognosis prediction, prediction models would be constructed, and PCR-based quantifications might be used in risk grading of adult AML patients.ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1) were downregulated in AML PB samples compared to healthy whole blood samples; ANXA3, CEACM1, RGL4, RILPL1, and HP showed increased expression levels in AML BM samples of the posttreatment stage compared to the diagnosis and/or recurrent stage. Also, the expression levels of ARG1, CEACAM1, CHI3L1, CRISP2, and CYP4F3 were demonstrated to be positively correlated with OS in an independent cohort. One of the hub genes, LTF, appeared on top of the TF prediction list, overlapping 10 hub genes. lncRNA-mRNA networks were constructed to exhibit the possible genetic regulatory mechanisms of adult CN-AML. LINC00671, which was linked to 17 mRNAs, has been widely reported as a tumor suppressor in various solid tumors. Clearly, this study identified the prognosis-specific biomarkers and the potential lncRNA-related regulatory mechanisms in AML. Our findings suggest CN-AML samples as good sources to investigate the relationship of RNA profiles and AML prognosis, and also provide a necessary groundwork for further exploration of the function and potential applications of these biomarkers as therapeutic targets for AML.In this study, we identified AML survival-specific mRNAs and lncRNAs using the WGCNA methodology based on CN-AML data. Eighteen mRNAs were screened as hub genes of the survival-specific mRNAs. Expression analyses in different cohorts of AML samples revealed 17 of the hub genes (


Deficits in hippocampal neurogenesis cause cognitive impairments in Alzheimer’s disease by compromising memory storage. Augmenting neurogenesis rescues memory by recruiting more new neurons with restored spine density and transcription profile. AD-linked genes regulate the memory-storing neurons. App, ApoE, and Adam10 were of the top differentially expressed genes in the engram. Collectively, these observations suggest that defective neurogenesis contributes to memory failure in AD.Hippocampal neurogenesis is impaired in Alzheimer’s disease (AD) patients and familial Alzheimer’s disease (FAD) mouse models. However, it is unknown whether new neurons play a causative role in memory deficits. Here, we show that immature neurons were actively recruited into the engram following a hippocampus-dependent task. However, their recruitment is severely deficient in FAD. Recruited immature neurons exhibited compromised spine density and altered transcript profile. Targeted augmentation of neurogenesis in FAD mice restored the number of new neurons in the engram, the dendritic spine density, and the transcription signature of both immature and mature neurons, ultimately leading to the rescue of memory. Chemogenetic inactivation of immature neurons following enhanced neurogenesis in AD, reversed mouse performance, and diminished memory. Notably, AD-linked   Nestin-driven CreERT2 conditional Bax deletion in 5XFAD mice  is characterized by progressive loss of memory and cognitive function. The mechanism underlying memory loss is largely unknown. Early memory impairments in AD affect episodic memory, spatial recognition, semantics, and visual orientation . The denFAD mice  rescued NestinCreERT2; Baxfl/fl mice  or NestinCreERT2; Baxfl/fl; 5XFAD (NBF) mice were treated with either tamoxifen (T-NB or T-NBF) or corn oil at 1 mo of age  and led to increased neurogenesis , greater survival of new neurons , as well as more immature neurons  in mice pre-injected with the viral vectors  was similar in the C-NB, C-NBF, and T-NBF mice  was significantly compromised in the C-NBF mice compared to the C-NB mice (+DCX+eYFP+ was significantly increased in T-NBF mice (+BrdU+eYFP+) recruited into the memory circuit in the different groups (+ DCX+ eYFP+)/total eYFP+ cells; To address whether immature neurons rescue memory in FAD by participating in memory formation, we sought to track neurons that were activated following memory acquisition. For this purpose, we used a Tet-off–based viral engram labeling kit composed of a cocktail of AAV9-cFos-tTA and AAV9-TRE-ChR2-eYFP  was similar among the three groups, suggesting that the total number of neurons recruited during memory retrieval was not changed due to the FAD genotype or level of hippocampal neurogenesis  revealed a reduced, albeit not statistically significant, number in C-NBF compared to C-NB, and a marked increase in the T-NBF group (+eYFP+Egr-1+) between the three groups was directly related to their recruitment during acquisition. Evidently, the number of immature neurons that was activated during retrieval, but not during acquisition (DCX+eYFP−Egr-1+), revealed no effect of treatment or genotype , an immediate early gene previously implicated as a memory retrieval proxy . This anogenesis . NotablyBF group . Notablygenotype . This regenotype . Examinagenotype . Taken t+eYFP+Egr-1+ cells in the DG of the C-NB, C-NBF, and T-NBF mice. Consistent with the notion that dendritic spines, which are important in memory formation, are impaired in AD  RV-hM4Di-eGFP 4 wk before CFC to specifically inactivate the newly mature neurons . To valit test, P = 0.048; C-NB versus C-NBF, P = 0.02; C-NB versus T-NBF, P = 0.98; eYFP in a uniformly traced area of the DG that included the hilus, subgranular, and the granular layers of the DG. Cells were defined as eYFP+ if they contained at least one eYFP read. Fisher’s exact test (FET) was used to compare the proportion of cells expressing each gene across pairwise groups for each cell type. t-distributed stochastic neighbor embedding (t-SNE) plots of the expression pattern in the DG of the three experimental groups. To gain an insight into the molecular profile of engram neurons, we compared the profile of eYFP+ neurons with one of the eYFP− neurons in the C-NB group. An array of genes critical for neuronal function, such as Syn1 (synapsin 1), Ndnf (neuron-derived neurotrophic factor), Ncam1 , Npy2r (neuropeptide Y receptor Y2), Slc6a5 (solute carrier family 6 member 5), Oprk1 (opioid receptor kappa 1), Mapk3 (mitogen-activated protein kinase 3), and Gabra1 , was upregulated in the eYFP+ neurons compared to eYFP− , Fos, Npas4 , Npy2r, Oprk1, Sst (somatostatin), Glul (glutamate ammonia ligase), Syn1, Slc17a8 (solute carrier family 17 member 8), ApoE (apolipoprotein E), Mapk3, Adam10 , Pvalb , and Gad2 (glutamate decarboxylase 2), were upregulated in eYFP+ mature neurons compared to eYFP− mature neurons in the C-NB mice , Slc6a1, Slc6a5, Slc17a8, Ncam1 , and Grin2b  were upregulated compared to eYFP− new neurons  new neurons in both C-NB and T-NBF mice  mice. Therefore, we sought to examine the signaling pathways of the engram in FAD compared to wild-type mice, and their alterations following augmentation of neurogenesis. In addition, we examined whether augmentation of neurogenesis affects the profile of the granule cells in the DG. Thus, we examined the transcription signature of immature and mature neurons recruited into the engram in the DG of C-NB, C-NBF, and T-NBF mice. For this purpose, C-NB, C-NBF, and T-NBF mice were injected with the engram cocktail, placed on a doxycycline diet, and subjected to CFC, as before. Mice were sacrificed 45 min after the test phase of CFC and brains were cryosectioned. Coronal sections were analyzed by spatial transcriptomics . 159 gento eYFP− . Interes as well . Some ge-NB mice . In eYFP neurons . Interes in C-NB . For the in C-NB , while aF engram . NotablyF engram . ExaminaF engram . The pro-NB mice . The exp-NB mice . Similar in C-NB . InteresNB group . Genes rNBF mice . Taken teYFP+ neurons located within the DG and hilus revealed a reduced distribution of eYFP+ neurons in C-NBF compared to C-NB and T-NBF  and Nefh (neurofilament heavy chain) were most downregulated in C-NB relative to C-NBF , App, Glul, Wfs1, Arc (activity-regulated cytoskeleton associated protein), and Lmo1 (LIM domain only 1) were the most downregulated in T-NBF relative to C-NBF (Grin1 (glutamate ionotropic receptor NMDA type subunit 1) and Syt6 (synaptotagmin 6) were most upregulated in T-NBF relative to C-NB, while Camk2a, Mapk3, Rprm (Reprimo), Per1 (period circadian regulator 1), and Pvalb were most downregulated . The data showed that augmenting neurogenesis contributed mostly to excitatory immature and mature neurons , eYFP+ mature neurons , and eYFP+ new neurons  is different than the ones used in their study . Different serotypes may have a distinct attachment receptor, capsid composition, and unique mechanism of endocytosis into the target cell. For example, AAV2 and AAV9 use different glycan moieties for initial attachment to the cell surface . PreviouA previous study used an optogenetic approach to interrogate stage-specific memory formation deficits in FAD and found that memories were acquired in the FAD model, yet these were not retrieved under normal physiological conditions but only following optogenetic activation of the engram in the DG . HoweverIn addition to its effect on the number of new neurons recruited into the memory circuit, we show that the characteristics of immature neurons recruited into the engram are compromised in FAD and rescued following augmented neurogenesis. We show that the density of dendritic spines in FAD is reduced compared to wild-type mice and rescued following augmentation of neurogenesis. The density of dendritic spines and their structure are profoundly implicated in learning and memory, cognitive resilience, and cognitive deficits in neurodegenerative disorders . InteresThe association between the hallmarks of AD, namely, amyloid deposition and neurofibrillary tangles, and memory deficits remains controversial. We have not observed a change in the amount of amyloid deposition in T-NBF compared to C-NBF (data not shown). Several studies demonstrated a link between tau pathology and vulnerability of ECXII neurons . ParticuOur study shows that enhancing neurogenesis in the intact young adult brain does not further improve performance in the CFC or NOL tasks. This may have several explanations. First, a ceiling effect, where the addition of extra new neurons will not improve what is already an intact learning process. Second, the CFC and NOL are not cognitively demanding for the exquisite role of the extra newly mature neurons for the capturing of further observable improvement. Indeed, previous studies showed that enhanced neurogenesis significantly improves pattern separation . Thus, aCamk2a, Mapk3, Pvalb, and Slc family members, repeatedly exhibited inter-group differential expression. Genes regulating neurogenesis, synaptogenesis, and plasticity, such as NeuroD1, Syt6, Ntn1, Tac2, Glul, Bdnf, and Rprm, showed the largest differences. Of note, App was one of the most significantly changed between the engrams of T-NBF/C-NBF. Modulation of App expression was most apparent in mature neurons of the engram. Notably,Adam10 was also one of the most significantly changing genes in the engram. Adam10 is thought to have α-secretase activity and is implicated in both neurogenesis and AD  and its complex is defective in FAD mice and AD patients . The acts and AD . Adam10 Apoe and Adam10 genes in the engram. Intriguingly, ApoE was most significantly differentially expressed in new neurons in the engram. In new neurons, ApoE and Bdnf were the most changing between C-NB and C-NBF. While deficit in Bdnf has been implicated in impairments in neurogenesis in FAD, this is the first time that ApoE is implicated in neuronal function and memory formation in the engram. In addition, Gabra1, Grin1, and Camk2a, known to play a major role in neurogenesis and synaptic function . 5XFAD mice were procured from The Jackson Laboratory (cat# 034848). NestinCreERT2 and Baxfl/fl mice were bred to yield Nestin; CreERT2; Baxfl/fl mice. Nestin CreERT2; Baxfl/fl were bred with the 5XFAD mouse model to yield NestinCreERT2; Baxfl/fl; 5XFAD mice. All the animals were maintained on C57bl/6 genetic background. Experiments used female mice only. Baxfl/fl; 5XFAD mice generated from these crosses were used to assess CreERT2-independent effects of tamoxifen on behavior. Recombination was induced by intraperitoneal injection of 2 mg of tamoxifen , once a day for 5 consecutive days at the age of 4 wk. Control groups received a similar volume of corn oil intraperitoneally once a day for 5 consecutive days.All animal experiments were approved by the University of Illinois at Chicago Institutional Animal Care and Use Committee, ACC Protocol # 17–123 (Lazarov). Mice were housed in a 12-h (06:00–18:00) light–dark colony room with ad libitum food and water. Behavioral testing was performed 3.5 mo following tamoxifen or vehicle injection. All experiments and analyses were performed blindly to genotype or treatment.3 chamber for 5 min for 2 d. On day 3, two similar objects were placed at two corners of one side of the box, 10 cm apart. The animals were allowed to explore the chamber for a maximum of 20 min or until the animals explored the two objects for a total of 30 s. “Exploring the object” was defined as the head direction pointing toward the object, while the animal is touching the object. On day 4, one of the objects was placed in a new location diagonally to the other one. The animals were allowed in the chamber for up to 20 min or until the two objects were explored for 30 s. The video was captured for every run and analyzed by Ethovision 10 software for time spent with each object. The box was cleaned with 70% alcohol between trials. The discrimination index was calculated as DI = (TN −TO)/(TN + TO), where DI = discrimination index, TN = time spent exploring the object at the new location, TO = time spent exploring the old location.Mice were habituated in an empty 50 × 50 × 50 cm3 chamber encased by isolation cubicles. The context had two plexiglass walls, two metal walls, and a stainless-steel grid floor (Coulbourn Instruments). On day 1 of the test, animals were placed in the chamber for 180 s. A 2-s foot shock of 0.65 mA was administered at the 148th s. Mice remained in the arena for 30 s to associate the context with the shock and then placed back in their home cages. On day 2 the animals were placed in the same chamber for 5 min without shock treatment. The motion of animals was captured by a digital video camera mounted above the test cage. FreezeFrame and FreezeView software (Actimetrics) were used for recording and analyzing the freezing behavior of the animals. Previously, manual scoring sessions carried out by investigators blinded to conditions were shown to be consistent with Freeze Frame scoring system. Then 70% ethanol was used to clean the grid floor and walls between runs.This test was conducted in a 17.8 × 17.8 × 30.5 cmTo test the effect of new neuron inactivation, 4 wk following stereotaxic injection of RV-HM4i-eGFP or RV-eGFP viral constructs into the DG, T-NBF mice were subjected to CFC, as above. To test for the overall effect of inhibiting new neuron activation on memory formation, mice received CNO 5 mg/kg  in drinking water starting 5 d before training and throughout testing. The calculation for CNO dosage was made based on the weight and the amount of water an individual mouse consumed daily so that each mouse would ingest about 5 mg/kg concentration of CNO per day. The amount of freezing was assessed by FreezeFrame software by Coulbourn.N = 12–16 per group) was conducted in a box having a light chamber with clear walls and a dark chamber with one third of the total box size. The dark chamber is opaque to visible light. Both the chambers were connected with an opening at floor level in the center to allow passage between the light and dark compartments. The light compartment was brightly illuminated. The animals were kept in dark for at least 1 h before testing. The apparatus was cleaned between trials. The mice were kept in the light chamber to freely explore both chambers for 5 min. Time spent in each chamber was assessed.Light–dark test was carried out to assess any anxiety-like behavior in animals. The light–dark test  and were packaged as previously described . Viral p−1 using 1 μl Hamilton microsyringe. The needle was lowered to the injection site and remained at the target coordinates for 5 min before injection. After the injection, the needle was kept for 10 min before being withdrawn to prevent the backflow of the virus cocktail. Animals were allowed to recover for 2 wk before behavioral experiments and kept on doxycycline-supplemented diet until the first day of CFC test to allow labeling of activated neurons during the memory acquisition phase. The animals were kept back on the doxycycline diet after the testing and were on diet for the remaining duration of the testing. All injection sites were verified histologically and immunohistochemically.Surgeries were performed at 4 mo of age and 2 wk before CFC. Mice were fed doxycycline-supplemented diet (40 mg/kg) starting 1 d before engram viral kit injection. Mice were anesthetized with 2.5% isoflurane and 100% oxygen mixture. Buprenorphine  was given before the start of surgery. The two viruses were mixed in equal volume and 1 μl of this viral cocktail was injected bilaterally into the DG ; dorsal-ventral [DV] = −2.5 mm) at a speed of 70 nl min7 titer, a gift from Jenny Hsieh, University of Texas at San Antonio, San Antonio, TX), or pMMLV-CAG-Tdtomato-P2A-hM4D 2 μl into two sites of the dorsal DG with following coordinates, site 1: , site 2:  bilaterally. Mice were allowed 3 wk of recovery period. 3 wk after surgery, mice were gently handled twice a day for 7 d before the CFC experiment. The CFC experiment was carried out 4 wk after virus injection based on previous findings that this time is the most optimal time window to manipulate the functions of the newborn cells (Mice at 5 wk of age were injected with tamoxifen (130 mg/kg) intraperitoneally for 5 consecutive days. At 3.5 mo of age, mice were anesthetized with 2.5% isoflurane using the VetTech incorporation apparatus and underwent a surgical procedure as described above, except this time they were injected with retrovirus carrying either -pMMLV-CAG-EGFP-p2A-hM4, or RV-pCAG-EGFP  was dissolved in 0.9% NaCl at a concentration of 10 mg/ml and injected at 100 mg/kg of body weight three times a day for three consecutive days at the age of 3 mo and 1 wk. 5 wk later, the animals were perfused, brains were sectioned, and the number of BrdUMice were deeply anesthetized by isoflurane and perfused transcardially with PBS followed by 4% paraformaldehyde. Brains were dissected and immersed in 4% paraformaldehyde overnight at 4°C followed by 30% sucrose solution for 3 d. Brains were sectioned (50 μm) coronally by a microtome  and stored −20° in a cryoprotectant solution. For immunostaining, sections were washed with 1× TBS three times, followed by blocking and permeabilization buffer  for 1 h. Sections were then incubated with primary antibodies at 4°C for 24 h. Following that, sections were washed with 1× TBS three times for 5 min each followed by 2 h of incubation with secondary antibodies. Sections were washed again in 1× TBS three times, 5 min each time, and counterstained with DAPI solution for 5 min. Sections were washed again twice for 5 min each, mounted on microscope slides, and topped with PVA-DABCO and coverslips. Images were taken on a confocal microscope . Analyzers were blind to the experimental conditions. Antibodies used for immunostaining were as follows: mouse anti-DCX , rabbit anti-DCX , mouse anti-NeuN , rat anti-BrdU , goat anti-GFP , rabbit anti–c-fos , and rabbit anti–Egr-1 . For mouse DCX and rat BrdU antibodies, antigen retrieval with 10 mM sodium citrate solution was performed before blocking. All secondary antibodies were obtained from Jackson ImmunoResearch laboratory. For secondary antibodies, anti-mouse cy-3 and cy-5 (both 1:500), anti-rabbit cy-3 and cy5 (both 1:500), anti-goat Alexafluor-488 , and anti-rat cy-3 and cy-5 (both 1:500) were used.Every sixth coronal section (50 μm) spanning through the whole DG region and 300 μm apart was used for cell count. Unbiased stereology was performed as previously described . Briefly+eYFP+Egr1+) were sampled and quantified in 30 μm perfused and fixed brain sections of C-NB, C-NBF, and T-NBF mice using Neurolucida 360 software. The spine classification algorithm was used as previously described  or Microsoft Excel. Statistical significance was assessed by Kruskal–Wallis test with Dunn’s post hoc test unless otherwise noted. One-variable experiments were analyzed by the two-tailed unpaired 2-size mold for cryosectioning . Brains were applied with optimal cutting temperature compound  at the base for stability, while the base mold was on dry ice. The brain blocks were placed in a bigger peel-away and the optimal cutting temperature compound was applied until solidified as a block ready for cryosectioning. Fresh frozen brain blocks were placed in −80°C. Blocks were cryosectioned on a cryostat  at 10 μm thickness and mounted on Superfrost Plus Microscope slide . Brain sections were stored at −80°C until shipment. The quality of RNA extracted from brain sections was checked and confirmed that each sample had RNA integrity number >7 . To validate the feasibility of quenching autofluorescence background, TrueBlack  was used for 30 s at room temperature. Brain sections were shipped to Cartana/10x Genomic (Sweden) for in situ sequencing.After 30 min of CFC testing, animals were deeply anesthetized with isoflurane followed by transcardial perfusion with ice-cold 1× PBS RNA-free  for 2 min. Brains were quickly isolated and placed gently into the sagittal brain matrix  and trimmed sagittally. This approach allowed two brains to fit into a 1 cmz-stack and flattened to 2D using maximum intensity projection. After image processing and decoding, the results were summarized in a csv file and gene plots were generated using MATLAB.All samples, as 10 μm cryosections placed on SuperFrost Plus slides, were sent to CARTANA Sweden (part of 10x Genomics) for library preparation, in situ barcode sequencing, imaging. and data processing. Briefly, tissues were fixed for 5 min, permeabilized with 0.1 M HCl and quenched with TrueBlack for 30 s. For library preparation, chimeric padlock probes (targeting directly RNA and containing an anchor sequence as well as a gene-specific barcode) for three pre-defined panels  as well as a custom panel of 41 genes were hybridized overnight at 37°C, then ligated before the rolling circle amplification was performed overnight at 30°C using the HS Library Preparation kit for CARTANA technology and following manufacturer’s instructions. All incubations were performed in SecureSeal chambers (Grace Biolabs). Optimal RNA integrity and assay conditions were also controlled using Malat1 and Rplp0 housekeeping genes by using the same protocol on serial sections. Quality control of the library preparation was also performed by applying anchor probes to detect simultaneously all rolling circle amplification products from all genes in all panels. Anchor probes were labeled probes with Cy5 fluorophore (excitation at 650 nm and emission at 670 nm). For barcode sequencing, adapter probes and sequencing pools  were hybridized to the padlock probes to detect the gene-specific barcodes through a sequence-specific signal for each gene-specific rolling circle amplification product. This was followed by imaging and performed six times in a row to allow for the decoding of all genes in the panels. Raw data consisting of 20× images from five fluorescent channels  were each taken as the Ache, Acta2, Adam10, Adamts2, Adarb2, Adora2a, Aldoc, Apoe, App, Aqp4, Arc, Arhgap36, Arntl, Atf4, Atxn1, Bace1, Bcl11b, Bdnf, Calb1, Calb2, Calca, Camk2a, Camk4, Cav1, Cblb, Cbln2, Cck, Ccr5, Cebpa, Chat, Chodl, Chrna2, Chrna6, Cnmd, Cpa6, Creb1, Crh, Crhr1, Crhr2, Crispld2, Cux1, Cux2, Dcn, Dcx, Deptor, Dicer1, Drd1, Egfr, Egr1, Eif2ak4, Eif2s1, Fev, Fezf2, Fos, Foxp2, Gabra1, Gabra2, Gad1, Gad2, Gfap, Gja1, Gls, Glul, Grin1, Grin2a, Grin2b, Hdac2, Homer1, Hpse, Igfbp4, Itgam, Kcnj8, Krt73, Lamp5, Laptm5, Lhx6, Lmo1, Lypd1, Map2, Mapk3, Mapt, Mbp, Mgll, Mmp9, Mup5, Ncam1, Ndnf, Nefh, Nes, Neurod1, Neurod6, Nf1, Npas4, Npy, Npy2r, Nrgn, Nrtn, Nt5c1a, Ntn1, Ntrk2, Nts, Oprk1, Oxt, P2rx3, Pcp4, Pdyn, Penk, Per1, Plat, Plch2, Plcxd2, Plp1, Ppp1r1b, Prox1, Psen1, Pthlh, Ptn, Ptprc, Pvalb, Rbbp4, Rbfox3, Reln, Rorb, Rprm, Rspo4, S1pr1, Satb2, Scn2a, Sema3e, Slc17a6, Slc17a7, Slc17a8, Slc6a1, Slc6a3, Slc6a4, Slc6a5, Sncg, Spp1, Sst, Syn1, Syt6, Sytl1, Tac1, Tac2, Tafa1, Tbr1, Th, Tpbg, Tph1, Trem2, Trh, Trhr, Trpv1, Tubb3, Unc5c, Vip, Vipr2, Wfs1, and eYFP.There were 159 genes of interest probed for in situ sequencing analysis: To assign reads to individual cells, the DAPI nuclei images were cell segmented using a custom MATLAB script . Briefly+ cells in each hemisphere and selecting those with the expected distribution in the DG for downstream analysis.To study cells exclusively in the DG, we used the DAPI microscopy image for each section to manually outline the DG (including the hilus) using the polygonal selection tool in FIJI 1.53c . The . To account for cases with zero gene expression and avoid ± infinity log2FC values, the smallest non-zero percent expression value across all genes was added to both groups as a pseudovalue. Benjamini-Hochberg false discovery rate was used to correct for multiple comparisons. Unless otherwise stated, unadjusted P values are shown.FET was used to compare the proportion of cells expressing each gene across pairwise groups for each cell type. Contingency tables were constructed for each gene by first pooling cell counts from each sample within the group and calculating the frequency of cells expressing/not expressing a given gene for each group. The percent of cells expressing each gene in each group was used to calculate the log2FC was calculated as described above. The binomial test was used to determine if the probability of the observed proportion of log2FC with the same and opposite directions was statistically significant.The union of the 20 genes with the lowest P values from FET for the C-NB/C-NBF and T-NBF/C-NBF comparisons were selected. For each gene, the logeYFP+/−, neuronal, and new or mature neurons) within each DG hemisphere. Euclidean distances were calculated using the vegan library (The fraction of cells expressing each gene was computed for each cell type ( library  and testMATLAB 9.0.0.341360 (R2016a) was used to perform cell segmentation. R version 3.6.3 (2020-02-29) was used for differential expression analysis and visualization.Supplementary information provides further validation measures of experimental models  and S2, Table S1lists antibodies used in this study.Click here for additional data file.


TTN locus).To identify and annotate transcript isoforms in the chicken genome, we generated Nanopore long-read sequencing data from 68 samples that encompassed 19 diverse tissues collected from experimental adult male and female White Leghorn chickens. More than 23.8 million reads with mean read length of 790 bases and average quality of 18.2 were generated. The annotation and subsequent filtering resulted in the identification of 55,382 transcripts at 40,547 loci with mean length of 1,700 bases. We predicted 30,967 coding transcripts at 19,461 loci, and 16,495 lncRNA transcripts at 15,512 loci. Compared to existing reference annotations, we found ∼52% of annotated transcripts could be partially or fully matched while ∼47% were novel. Seventy percent of novel transcripts were potentially transcribed from lncRNA loci. Based on our annotation, we quantified transcript expression across tissues and found two brain tissues  expressed the highest number of transcripts and loci. Furthermore, ∼22% of the transcripts displayed tissue specificity with the reproductive tissues  exhibiting the most tissue-specific transcripts. Despite our wide sampling, ∼20% of Ensembl reference loci were not detected. This suggests that deeper sequencing and additional samples that include different breeds, cell types, developmental stages, and physiological conditions, are needed to fully annotate the chicken genome. The application of Nanopore sequencing in this study demonstrates the usefulness of long-read data in discovering additional novel loci  and resolving complex transcripts (e.g., the longest transcript for the  Gallus gallus domesticus) is the most widespread domesticated farm animal for egg and meat production, with a total population of 37.2 billion for the year 2020 (http://www.fao.org/). Besides its agronomic importance, chicken has contributed greatly to biological studies on evolution, development, and immunology. In 2004, the first draft whole chicken genome was assembled with an estimated set of 20-23,000 protein-coding genes (PCGs) (9 bp), and the evolution of coding and noncoding genes in vertebrate genomes. Additional efforts including high-resolution genetic linkage maps, radiation hybrid maps, targeted genome sequences, allowed us to know the chicken karyotype, which consists of 38 pairs of autosomes and a pair of sex chromosomes (chromosomes W and Z) (Chicken (s (PCGs) . This efW and Z) . Since tW and Z) .https://uswest.ensembl.org) and National Center for Biotechnology Information  regularly update the chicken genome annotation. For instance, Ensembl release (V102) includes 16,779 PCGs and 39,288 transcripts, representing 2.34 transcripts per gene, which is quite low compared to human with ∼10 transcripts per gene. The high estimate in human can be attributed to several large global efforts, such as GENCODE, which is part of the ENCODE (ENCyclopedia Of DNA Elements) consortium which aims to identify and classify all gene features in human and mouse genomes. In farm animals, likewise, the Functional Annotation of ANimal Genome (FAANG) consortium was formed in order to improve the annotation of livestock genomes . All tissues were from one of two FAANG pilot projects (FarmENCODE) . In brieRNA extraction and library preparation were performed as described in https://nanoporetech.com/), NanoPlot (v1.0.0) software was used to summarize read length and average quality, among others. Then, the Pychopper v2 software (https://github.com/nanoporetech/pychopper) was employed to identify and orient full-length reads, which were mapped to reference genomes  with options of “-ax splice -uf -k14 -G 1000000” using the minimap2 software  tool (software . We discsoftware . The HTSsoftware  was usedsoftware . Principsoftware .https://github.com/nanoporetech/pipeline-nanopore-ref-isoforms). Briefly, the oriented full-length reads with fastq format were pooled together and then mapped to the Ensembl annotation  using minimap2 (Comparing predicted transcripts to previous annotations section).To predict transcripts, we used a computational pipeline supported by the Oxford Nanopore Technology community  and CPP2  and NCBI reference (V105) annotations using GffCompare (version 0.11)  and clashttps://github.com/nanoporetech/pychopper) to the predicted transcriptome using minimap2 (v2.1)  . The tra2 (v2.1) . Based o2 (v2.1) .TSI) (xi is an average of transcript expression (TPM) in a given tissue, n is the number of tissues. Transcripts were then categorized as tissue-specific (TSI ≥0.8), broadly expressed (TSI <0.5), or biased towards a group of tissues (0.5 ≤ TSI <0.8). To reveal functional biology of tissue-specific transcripts, we extracted tissue-specific transcript sequences and aligned them to the SwissProt  using the Diamond blastx tool (v2.0.11.149)  :TSI=max.11.149) . Next, w.11.149) . The fal.11.149)  was usedTo detect differential alternative splicing (DAS) events, we employed the LIQA software . Based oTo annotate transcripts of the chicken genome, we sequenced 68 samples that covered 19 different and diverse tissues collected from six individual adult White Leghorn birds  . The 19 p = 1.30 × 10−11) with the exception of Cecum_CA, which had extremely low number of sequencing reads , suggesting the unexpected clustering is possibly due to insufficient sequencing depth  revealed that samples generally clustered according to the origin of tissue as expected . Moreoveng depth . Furtherhttps://github.com/nanoporetech/pipeline-nanopore-ref-isoforms). As a result, 79,757 transcripts in 54,551 loci were identified. After filtering out transcripts on unplaced scaffolds, as well as those with exon coverage <100% and read depth <2, we obtained 74,665 transcripts in 50,569 loci, of which there were 45,132 multi-exon and 29,533 single-exon transcripts. Moreover, we required multi-exon transcripts with expression TPM >0.1 and single-exon transcripts with expression TPM >1 in at least 2 samples of a tissue. By doing so, 61,556 transcripts in 45,284 loci were remained. To further exclude potential artifacts, we compared assembled transcripts with NCBI (V105) and Ensembl (V102) reference annotations. The results are shown in To assemble potential transcripts, we identified, oriented, and trimmed full-length reads using Pychopper v2. Further analysis indicated that all full-length reads had poly(A) tails with average length of 19 bases (range was 8-637 bases) . Then, STTN gene encoding the giant protein titin   . This pr 99.99%) . Moreove 99.99%) .To determine the coding potential of the predicted transcripts, we employed CPC2 and TransDecoder. The former predicted 21,984 transcripts at 12,999 loci with coding potential, and the latter one predicted open reading frames for 30,727 transcripts corresponding to 19,306 loci. In total, we predicted 30,967 uniquely potential coding transcripts at 19,461 loci, representing 1.6 transcripts per locus . FurtherWe compared our predictions to two reference annotations and found the number of transcripts per locus of our annotation (∼1.4) was lower compared to both reference annotations , but we predicted ∼20,000 more loci, of which a substantial proportion are lncRNA loci . At the Next, we sought to identify tissue-specific transcripts. By quantifying transcript expressions, we found the number of expressed transcripts and loci ranged from 14,841 (liver) to 28,648 (cerebellum), and from 10,285 (liver) to 21,662 (cerebellum), respectively . The tisCYB561A3 gene showing DAS between heart and testis  could detect higher number of transcripts as expected . In addiA well-annotated chicken genome is essential in associating genetic variation to phenotypic variation, and there are a number of ongoing efforts through the Functional Annotation of Animal Genomes (FAANG) consortium , which pThe number of predicted loci with a transcript in this study was substantially higher than two reference annotations , while our prediction is lower than Compared to the reference annotations, we observed a higher percentage of novel loci (∼47%) compared to a parallel effort in cattle  where 6%The number of transcripts reported by this study, other studies, and reference genome annotations varies widely, ranging from 27,955 to 74,665. One possible explanation is differences in sequencing depth. Our study generated on average 300,000 reads per sample, ranging from 99,798 (Spleen_CD) to 686,752 (Spleen_CC), while Previous reports  have est


Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is a global public health concern. One-carbon (1C) metabolism plays a crucial role in the occurrence and development of multiple cancer types. However, there are limited studies investigating 1C metabolism in LUAD. This study aims to evaluate the prognostic value of 1C metabolism-related genes in LUAD and to explore the potential correlation of these genes with gene methylation, the tumor microenvironment, and immunotherapy.Methods: We identified 26 1C metabolism-related genes and performed a Kaplan-Meier and Cox regression analysis to evaluate the prognostic value of these genes. Consensus clustering was further performed to determine the 1C metabolism-related gene patterns in LUAD. The clinical and molecular characteristics of subgroups were investigated based on consensus clustering. CIBERSORT and ssGSEA algorithms were used to calculate the relative infiltration levels of multiple immune cell subsets. The relationship between 1C metabolism-related genes and drug sensitivity to immunotherapy was evaluated using the CellMiner database and IMvigor210 cohort, respectively.Results: The expression levels of 23 1C metabolism-related genes were significantly different between LUAD tumor tissues and normal tissues. Seventeen of these genes were related to prognosis. Two clusters (cluster 1 and cluster 2) were identified among 497 LUAD samples based on the expression of 7 prognosis-related genes. Distinct expression patterns were observed between the two clusters. Compared to cluster 2, cluster 1 was characterized by inferior overall survival (OS) , increased tumor mutation burden , high expression of PD-1 (p < 0.001) and PD-L1 (p < 0.001), as well as enhanced immune infiltration. 1C metabolism-related genes were positively correlated with the expression of methylation enzymes, and a lower methylation level was observed in cluster 1 (p = 0.0062). Patients in cluster 1 were resistant to chemotherapy drugs including pemetrexed, gemcitabine, paclitaxel, etoposide, oxaliplatin, and carboplatin. The specific expression pattern of 1C metabolism-related genes was correlated with a better OS in patients treated with immunotherapy .Conclusion: This study highlights that 1C metabolism is correlated with the prognosis of LUAD patients and immunotherapy efficacy. Our findings provide novel insights into the role of 1C metabolism in the occurrence, development, and treatment of LUAD, and can assist in guiding immunotherapy for LUAD patients. Lung cancer remains one of the most prevalent cancer types and the most lethal cancer type worldwide . Lung caMTHFD2 expression is associated with poor prognosis in hepatocellular carcinoma and colorectal cancer  metabolism, also known as folate metabolism, is involved in multiple physiological processes, such as biosynthesis, amino acid homeostasis, epigenetic maintenance, and redox defense . It has l cancer . SHMT2 hgression . In addirognosis . Becauserognosis .It has been shown that 1C metabolism can affect the function of immune cells, especially the activation of T cell . Immune 1C metabolism can support methylation reactions by generating 1C units . DNA and RNA methylation has been widely considered to be the best-characterized epigenetic modifications, and play an important role in the occurrence and development of tumors. DNA methylation occurs at the 5-position of cytosine (5 mC), and transcriptionally regulates the expression of target genes . RNA metAlthough 1C metabolism has been shown to have important functions in the process of methylation and the resistance to pemetrexed, its role in the occurrence, development, and treatment of LUAD remains unclear. In the present study, we found that 1C metabolism is associated with the prognosis of LUAD and the effect of immunotherapy. 1C metabolism-related genes are potential biomarkers of prognosis of LUAD and can help to guide immunotherapy in LUAD patients.https://xenabrowser.net/datapages/) (https://www.ncbi.nlm.nih.gov/geo/), including GSE3141, GSE29013, GSE31219, GSE31210, GSE37745, and GSE50081. After normalizing the datasets and removing batch effects, the expression profile data was used for the subsequent analysis.RNA-seq profiles (Counts and FPKM format), somatic mutation, DNA methylation, and phenotype data from The Cancer Genome Atlas (TCGA) LUAD cohort were downloaded from the UCSC Xena database (apages/) . Six GSEPHGDH, PSAT1, PSPH, FTCD, SHMT1, SHMT2, MTHFD2L, MTHFD2, MTHFD1L, MTHFD1, GCAT, SARDH, DMGDH, GNMT, BHMT, ALDH7A1, CHDH, TYMS, MTR, MTHFR, GART, ATIC, ALDH1L1, ALDH1L2, DHFR, and MTFMT.Based on the findings of previous studies, 26 1C metabolism-associated genes were identified . These gt‐test. A Kaplan-Meier analysis based on the optimal cutoff point was performed using R packages  to evaluate the clinical relevance of 1C metabolism-associated genes. A univariate Cox proportional hazard regression analysis was performed to identify the risk factors among these genes. Genes with p < 0.05 in the Kaplan-Meier analysis or univariate Cox proportional hazard regression analysis were considered prognosis‐related genes.The expression level differences of 1C metabolism-associated genes between 509 LUAD samples and 58 adjacent normal tissues were tested using a Student’s p-value and hazard ratios, including TYMS, DHFR, MTHFD1L, MTHFD1, ATIC, GNMT, and CHDH. K-means consensus clustering with these 7 genes was performed to identify subgroups in TCGA cohort. Consensus clustering was employed using the R package “ConsensusCluster” (n = 248) and cluster 2 (n = 249). Similarly, consensus clustering was also performed in GEO cohort, and patients were divided into two clusters, including cluster 1 (n = 397) and cluster 2 (n = 437). Kaplan-Meier analysis was used to assess OS differences between the two subgroups in TCGA cohort and GEO cohort, respectively.According to the results of the Kaplan-Meier analysis and univariate Cox proportional hazard regression analysis, 7 prognostic genes in univariate analysis were actually selected based on Cluster” . The detTYMS, DHFR, MTHFD2L, MTHFD1, ATIC, GNMT, and CHDH. The risk score of each patient was calculated through the equation: risk score = sum of coefficients × expression level of prognostic genes. The LUAD patients were identified as two subgroups based on the median risk score, including high-risk group and low-risk group.The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the prognostic genes of 1C metabolism. According to the result of LASSO regression, 7 prognostic genes were finally selected, including p-value cutoff = 0.05.To determine the different biological processes between the two subtypes, a gene set enrichment analyses (GSEA) was conducted in the Hallmark gene set “c5.all.v7.0.entrez.gmt” of MSigDB using the R package “ClusterProfiler” . The parhttp://cibersort.stanford.edu/) was used to assess the abundances of 22 immune cell types based on the RNA-seq profile of LUAD. The relative abundance of 28 distinct leukocyte subsets was also calculated through ssGSEA using the R package “GSVA”. In addition, the immunoscore of each patient was also calculated through the R package “estimate”.The infiltration level of immune cells was calculated through cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) and single-sample gene set enrichment analysis (ssGSEA) . CIBERSOTo assess somatic mutations between the different subtypes, the somatic mutation profile of LUAD patients was downloaded from the UCSC Xena Database . The somhttps://discover.nci.nih.gov/cellminer/home.do) and was further analyzed with R Software (version 4.1.2). The correlation between 1C metabolism-associated genes and drug sensitivity was calculated. The following selection criteria were used: Food and Drug Administration approval of the therapeutic or inclusion of the therapeutic in clinical trials, and p < 0.05.Drug sensitivity analysis was performed using the CellMiner Database . The RNATo validate the value of 1C metabolism-associated genes in immunotherapy prediction, the IMvigor210 cohort was used to investigate the relationships between 1C metabolism-associated genes and immunotherapy response . Data frt-test and Wilcoxon rank-sum test, respectively. Two-sided Fisher’s exact tests were performed to analyze contingency tables. Survival analyses were performed using the Kaplan-Meier method, and the log-rank test was used to evaluate the difference between groups. A correlation analysis was performed using a Pearson correlation test. Multivariate analyses were conducted using a Cox regression model to identify the independent risk factors. A p-value < 0.05 was considered statistically significant.Statistical tests were carried out with R (version 4.1.2), SPSS 22.0 , and GraphPad Prism 9.0. For quantitative data, statistical significance for normally- and nonnormally-distributed variables were estimated using a Student’s PSAT1, PSPH, FTCD, SHMT1, SHMT2, MTHFD2L, MTHFD2, MTHFD1L, MTHFD1, GCAT, DMGDH, ALDH7A1, CHDH, TYMS, GART, ATIC, ALDH1L1, ALDH1L2, DHFR and MTFMT  was characterized by a high expression of high-risk genes while cluster 2 (n = 249) was identified by a high expression level of protective genes , advanced stage (p = 0.036), and smoking status (p < 0.001) were more frequently observed in cluster 1 (n = 397) and cluster 2 (n = 437)  . Comparen = 437) . Kaplan-TP53 was the most commonly mutated gene—with a frequency of 63%—followed by TTN, CSMD3, MUC16, and RYR2  were identified among patients in the IMvigor210 cohort . A Kaplaerior OS . In addi disease . In addi disease .1C metabolism has been shown to play a role in the occurrence, development, and treatment of multiple cancers. Many 1C metabolic enzymes have been observed upregulated in cancer tissues compared with adjacent normal tissues, and are closely associated with cancer prognosis. However, the literature on 1C metabolism in LUAD patients is sparse. In the present study, we evaluated the expression levels of 1C metabolism-related genes and the correlation with LUAD prognosis. Unsupervised clustering analysis was performed to classify the samples into cluster 1 and cluster 2. We found that cluster 1 was characterized by inferior OS, increased TMB, high PD-1 and PD-L1 expression, as well as enhanced immune infiltration. In addition, 1C metabolism-related genes were positively correlated with the expression of methylation enzymes, and lower methylation levels were observed in cluster 1. Patients in cluster 1 were also resistant to chemotherapy drugs, including pemetrexed, gemcitabine, paclitaxel, etoposide, oxaliplatin, and carboplatin. We also found that 1C metabolism-related genes were positively correlated with immunotherapy sensitivity.PSAT1, PSPH, FTCD, SHMT1, SHMT2, MTHFD2L, MTHFD2, MTHFD1L, MTHFD1, GCAT, DMGDH, ALDH7A1, CHDH, TYMS, GART, ATIC, ALDH1L1, ALDH1L2, DHFR, and MTFMT, while the downregulated genes included GNMT and MTHFR. A Kaplan-Meier analysis revealed that 17 genes were associated with prognosis. Among these genes, nine genes were identified as risk factors while the other eight genes were considered protective factors. A univariate Cox regression analysis identified six risk-associated genes and four protective genes. Consensus clustering was performed, and 497 LUAD patients were classified into two clusters. We found that, compared with cluster 2, cluster 1 exhibited the opposite expression pattern and a worse OS.In our study, 1C metabolism-related genes were selected according to previous studies. The expression levels of these genes were evaluated, and twenty genes were upregulated in tumor tissues, while two genes were downregulated. The upregulated genes included TP53, TTN, CSMD3, LRP1B, ZFHX4 and XIRP2 were significantly higher in the high-risk group compared with the low-risk group. In addition, a heavier TMB was also observed in the high-risk group. Furthermore, the GSEA results suggested that the pathways, which were associated with cell cycle and chromosome segregation, were significantly enriched in the high-risk group. TP53 (p53) is one of the most common tumor suppressor genes in human cancers. The p53 protein plays an antitumor role by repairing DNA damage, regulating metabolism, normalizing reactive oxygen species levels, modulating expression of non-coding RNAs, and promoting autophagy or ferroptosis (TTN, CSMD3, LRP1B was also positively correlated with response rate to immunotherapy (The genetic landscape of these two groups was also investigated. We observed that somatic mutations were more frequent in the high-risk group. The mutation rates of roptosis . TP53 muroptosis . In addiotherapy . Therefo+ T cells, CD4+ T cells, NK cells, Type 2 T helper cells and M1 macrophages were significantly upregulated in the high-risk group, while regulatory T cells were downregulated. Furthermore, the expression of PD-1 and PD-L1 was significantly upregulated. The TME is closely related to the occurrence and progression of tumors, and influences immunotherapy efficacy (To investigate the difference in the TME between these two groups, CIBERSORT and ssGSEA were performed. The results revealed that CD8efficacy . A previefficacy . In addiefficacy . Previouefficacy . The actefficacy . TherefoSEPT9 and KLF13, while hypomethylation occurs in HNRNPR. SEPT9 and KLF13 have been shown to be antitumor genes in previous studies (HNRNPR contributes to the proliferation and metastasis of gastric cancer (HNRNPR hypomethylation leads to tumor proliferation and metastasis. According to these findings, we speculated that 1C metabolism may play an important role in both methylation and demethylation. On the one hand, the high expression level of 1C metabolism related genes accelerate the generation of 1C units in tumor cells. The abundant methyl groups provide the needs of the methylation of DNA, RNA and proteins, which have been proved by numerous studies (1C metabolism generates 1C units to support methylation reactions. To investigate the relationship between 1C metabolism and DNA and RNA methylation, we calculated the correlation of enzymes in the 1C metabolism pathway with 49 methylation enzymes, and the results suggested that 1C metabolism genes are generally positively correlated with methylation enzymes, such as “writers”, “readers”, and “erasers”. A previous review suggests that RNA modifications, including m6A, m1A and m5C, plays an important role in the occurrence and development of lung cancer . Conside studies . The hypc cancer , whereby studies . On the  studies . We therMTHFD2 overexpression is involved in resistance to pemetrexed (We further explored the potential correlation between 1C-related gene expression and drug sensitivity in the CellMiner database. Based on the expression pattern of cluster 1, tumor cells exhibited lower sensitivity to gemcitabine and oxaliplatin, but a higher sensitivity to 6-mercaptopurine. However, tumors with a cluster 2 expression pattern were sensitive to paclitaxel and carboplatin, while resistant to etoposide and lapatinib. Gemcitabine, paclitaxel, etoposide, oxaliplatin, and carboplatin are common drugs for the treatment of NSCLC, however, only a subset of patients benefit from these drugs . Lapatinmetrexed . We founDHFR, TYMS, GART, MTHFD2 and SHMT1 were correlated with a superior OS to immunotherapy. The expression levels of TYMS, GART, and MTHFD2 were also higher in patients with a complete or partial response compared with patients who had stable or progressive disease. Thus, we speculated that 1C metabolism-related genes may play a role in immunotherapy response and lead to a clinical benefit from immunotherapy.We speculated that patients in cluster 1 could benefit from immunotherapy. We therefore investigated the relationship between 1C metabolism-related genes and immunotherapy in the IMvigor210 cohort. in vivo and in vitro experiments are needed to explore the potential mechanisms.Several limitations exist in our study. Although the results were substantiated in both TCGA and the IMvigor210 cohort, they were not confirmed in LUAD patients who were treated with immunotherapy because of the insufficient transcriptome data from clinical trials. Consequently, different data sets were used in this study. To reduce bias, external validation in larger cohorts is required to validate these findings. Lastly, Taken together, our study demonstrates that 1C metabolism-related genes possess potential as therapeutic targets as well as biomarkers of prognosis of immunotherapy in LUAD. Based on the expression pattern of 1C metabolism-related genes, LUAD patients can be classified into two subtypes. Specific subtype characteristics provide information for LUAD clinical management and decision-making. Our findings provide new insight into the mechanisms associated with poor LUAD prognosis, predict efficacy of several therapeutic drug, as well as assist in identifying biomarkers for immunotherapy in LUAD patients.