StRab5b was cloned from potato, and its biological information, expression profile and induced expression level, overexpression and gene silencing were examined on regulating potato resistance to Phytophthora infestans using PCR, qPCR and Virus-induced gene silencing (VIGS). Our results indicate that the amino acid of StRab5b shows the highest and lowest homology with NbRab5b in N. benthamiana and StRab in potato respectively. StRab5b expression varied among different potato tissues and varieties, and was induced by P. infestans infection. Transiently ectopic expression of StRab5b in N. benthamiana enhanced its resistance to P. infestans, whereas, silencing of StRab5b and its homologous gene facilitated pathogen infection in potato and N. benthamiana respectively. Furthermore, stable expression of the StRab5b gene in potatoes enhanced its redox-stress response capacity, as manifested by the accumulation of H2O2 in infected leaves and subsequent increase in the activity and expression of ROS scavenging enzymes, thereby attenuating the development of P. infestans and ultimately reducing the lesions on infected potato leaves. In addition, the LOX gene transcripts and JA level were upregulated rapidly after inoculation with P. infestans. Collectively, our results suggest that StRab5b positively regulates the resistance against potato late blight (PLB) via JA-mediated defense signaling pathway.Rabproteins are the largest members of the small G protein family and are widely distributed in eukaryotes. It comprises eight subfamilies and is responsible for regulating vesicle transport, plant growth and development, and biotic and abiotic stress responses. In this study, the small G protein gene  However, its tuber yield and quality are devastatingly affected by PLB caused by P. infestans. In China, in the early 1950s and the 1960s, PLB caused yield losses amounting to approximately 1 billion dollars   located on the cell membrane recognize the pathogen associated molecular patterns (PAMPs) of ty, PTI) . Microbety, PTI) . Then, Mty, PTI) . Resistaty, PTI) .StbZIP61 is involved in the SA signaling pathway that regulates the defense response of potato against P. infestans , JA, and ethylene (ET) biosynthesis are activated through PTI, ETI, and systemic acquired resistance (SAR). The accumulation of SA, JA and ET leads to enhanced resistance to pathogens in plants . Particunfestans  and enhanfestans . Similarnfestans .2O2 were also represented in potato transgenic plants after inoculation with P. infestans  occurs, leading to programmed cell death (PCD) and HR. However, cells have evolved a set of ROS-scavenging enzymes, including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) to balance cellular ROS levels . Recent nfestans . Zhang enfestans . In concRab genes have been cloned from rice (Oryza sativa L.) and 57 from Arabidopsis thaliana (Rab gene StRab5b is located on the 11th chromosome of the genome (https://solgenomics.net/locus/82544/view). Previous studies have shown that Rab proteins regulate both abiotic and biotic stress responses in plants, plant hormone-mediated signal transduction, plant growth and development, and vesicular trafficking . The active OsRac1 caused HR responses and substantially reduced disease lesions against rice blast fungus  are used to dissociate the GDP to form an active GTP-RAB-effector, and GTPase activating proteins (GAPs) are used to decompose GTP into GDP. GDP-RAB combines with GDI to form inactive GDP-RAB-GDI, thus entering the next regulation cycle . RAB prothaliana . In potafficking . Researcse genes . Small Gutophagy . In prevt fungus . In Arabsicicola . Additiot fungus . Moreovenfestans , indicatStRab5b in the regulation of potato resistance against late blight. For this purpose, the StRab5b gene was cloned, gene sequences and expression profile were analyzed; transient expression and gene silencing were performed in N. benthamiana and potato. Stable expression of StRab5b in potatoes was obtained and used to confirm its role in regulating potato resistance against P. infestans. These results lay a stone for resistant breeding against potato late blight.In this study, we aimed to elucidate the role of the small G protein 22.1in vitro on Murashige and Skoog (MS) medium, under a luminous flux intensity of 3,000–4,000 Lx, 80% relative humidity, and a photoperiod of 16 h/8 h light/dark, at 25°C. The P. infestans strain (14-3) was kindly provided by Prof. Francine Gover, Wageningen University and Research (WUR), Netherlands. Tubers of six potato varieties ‘Zihuabai’ (ZHB), ‘Desiree’ (DXR), ‘Longshu No.7’ (L7), ‘Zhongshu No.4’ (ZS4), ‘Atlantic’ (DXY) and ‘Shepody’ (XPD), were planted in a nutrient pot and cultivated in a chamber under a luminous flux density of 18,000 Lx, 75% relative humidity, and a photoperiod of 16 h/8 h light/dark, at 25°C. These plants were used for expression profile analysis. Desiree seedlings cultivated in pots for 20 d were selected for induced expression.Potato cultivar ‘Desiree’ was used for gene amplification and genetic transformation. Plants were grown Primers were designed using Primer 5.0 software and synthesized by the Beijing Hooseen Biotechnology Company, China. The primer sequences used in this study are listed in 2.22O was added to bring the volume to 1,000 mL. YEB liquid medium  with pH 7.0 was prepared, and H2O was added to bring the volume to 1,000 mL. Agar powder (15 g) was added to each mixture media to prepare the corresponding solid medium. P. infestans was induced to produce zoospores at 4°C for 4 h. Zoospore suspensions were adjusted to a concentration of 5 × 105 mL-1 sporangia using a hemocytometer . Approximately 10−15 μL of spore suspension was inoculated on plant leaves.Luria-Bertani liquid medium  was prepared, and H2.3St Rab5b and PDS), and GAPDH was used as a reference gene. Q-PCR was performed on a LightCycler  using the SYBR Premix Ex Taq II Kit . We analyzed the expression levels of ACS, LOX, NPR1, SOD, APX1, POD, CAT2, and PIO and used Actin as a reference gene.Relative expression quantitation was performed using Quantity one software for semi-quantitative RT-PCR , and genomic DNA was extracted using the DNAiso reagent (TaKaRa). The quality of RNA and DNA was determined by performing gel electrophoresis on 1% agarose gels. The first cDNA chain was synthesized according to manufacturer instructions of a HiScript II reverse transcription Kit .2+ plus), 4 μL of dNTP Mix (2.5 mM), 1 μL of Primer Rab5bF/Rab5bR, 1 μL of cDNA template, 0.5 μL of Prime STAR HS DNA Polymerase, and 32.5 μL of ddH2O. Target fragments were purified using the EasyPure Quick Gel Extraction Kit  according to manufacturer’s instructions. Plasmid extraction was performed according to the manufacturer’s instructions of the TIANprep Mini Plasmid Kit II (Beijing TIANGEN Biotech Company). PLG-Rop and pBIA1300-221 were provided by Prof. Yan Zhao, from the Institute of Genetics and Development, Chinese Academy of Sciences, Beijing, China. WUR kindly provided the P. infestans strains, pTRV1 and pTRV2 vectors. Fastdigest endonucleases KpnI, BglII, XbaI, and SacI were purchased from Thermo Fisher Scientific, .The PCR-amplification mixture (50 μL) included 10 μL of 5X Prime Star Buffer (MgRab or Rab5b sequences of Oryza sativa (AF323991), Nicotiana benthamiana (DQ335217), Solanum tuberosum (ABK96799 and AK323357), Hevea brasiliensis (KC577146), Physcomitrella patens (AB379973), Phoenix dactylifera (AK287492), Nicotiana sylvestris (X63875), Mangifera indica (KF768563), Athaliana (D89824), Selaginella moellendorffii (KF516567), Medicago sativa (X79278), Triticum aestivum (X59133), Lotus japonicus (Z73939), and Medicago truncatula (BT147767) were retrieved from Genbank separately.Phylogenetic analyses were performed with DNAMAN software. The StRab5b gene was linked to the pEASY-Blunt Simple cloning vector and transferred into DH5α cells . Positive DH5α colonies were screened and sequenced. Target gene purification was performed using the EasyPure Quick Gel Extraction Kit (TransGen Biotech Company). Recombinant vectors PLG-Rop and pEASY-Blunt Simple-StRab5b were digested with BglII and KpnI to obtain the PLG-StRab5b vector. Recombinant plasmid PLG-StRab5b and vector pBIA1300-221 were digested with XbaI and SacI to obtain the recombinant plasmid pBIA1300-StRab5b. Plasmid pBIA1300-StRab5b was transferred into Agrobacterium tumefaciens GV3101 for preservation.The 2.5A1 = 1/4π × length × width.The length and width of the leaf lesions formed on the plant leaves were measured using an electronic caliper . Each lesion was measured three times . The les2.6P. infestans for analysis of the expression profile of StRab5b was described as follows. The seedlings of different potato varieties were cultured in a pot (10 cm height * 10 diameter) for 50 d. The abaxial surface of potato leaves was scratched with sterile toothpicks and inoculated with 10 uL zoospore suspension (5 × 105 mL-1) of P. infestans on one side; the other side was inoculated with water as control. The inoculated leaves were placed in a plastic box with wet filter paper on the bottom and kept in an incubator .Inoculation method of StRab5b after inoculation with P. infestans: Potato seedlings (‘Desiree’) were planted in a pot (10 cm height * 10 diameter) for 20 d, then inoculated with 10 μL of sporulation of P. infestans (5 × 105 mL-1). Then, inoculated leaves were collected at 0, 12, 24, 48, 72, 96, and 108 h post-inoculation (hpi). Using potato GAPDH as a reference gene, RT-PCR was performed to detect the relative expression levels of StRab5b, and the results were analyzed using Quantity one software.Induction of 2.7A. tumefaciens containing pBIA1300-StRab5b and pBIA1300 were inoculated in 10 mL YEB liquid medium (100 μg/mL rifampicin and 50 μg/mL kanamycin). The oscillating culture was incubated overnight at 180 rpm, followed by centrifugation for 2 min at 7,104×g to collect the bacteria; then, the culture was resuspended in a liquid mixture containing 150 μM acetyleugenone, 10 mM MgCl2, and 10 mM MES. The bacterial solution was suitable for use when the OD600 value reached 0.5–0.6. The bacterial liquid (15–20 μL) was drawn using a needle-free syringe, injected into the right side of the abaxial surface of the N. benthamiana leaf, and incubated for 24 h  at 21°C. The N. benthamiana leaves were inoculated with P. infestans spores and incubated for 4 h at 4°C before being transferred to a 21-23°C incubator with 80% humidity. The lesion area was measured at different inoculation time points.StRab5b were digested with XbaI and SacI to construct the silence-expression vector pTRV2-Rab5b (98% amino acid identity with Rab5b of N. benthamiana). The Agrobacterium tumefaciens vector pTRV1 was provided by WUR. Plasmid pTRV2-Rab5b was then transferred to A. tumefaciens GV3101. A. tumefaciens plasmids were resuspended with VIGS mixture containing 20 mM MgCl2, 100 mM MES, and 20 mM acetosyringone. The photobleaching phenotype was obtained by inhibiting the expression of endogenous phytoene desaturase gene (PDS). A 536 bp fragment of StPDS (95% amino acid identity with PDS of N. benthamiana) was linked with pTRV2 and transformed into A. tumefaciens GV3101 (pTRV2-PDS). Thereafter, A. tumefaciens containing pTRV1 was mixed with pTRV2-PDS or pTRV2-StRab5b at a 1:1 ratio after the OD600 value reached 0.5−0.6. A needle-free syringe was used to draw 20 μL of the bacterial suspension to inject into N. benthamiana and potato leaves. Three to four leaves of each plant were inoculated. After photobleaching, leaf samples were collected to identify the relative expression levels of PDS, StRab5b and StRab5b homologous gene using RT-PCR, after 25 d (N. benthamiana) and 60 d (potato). GAPDH was used as a reference.Recombinant vectors pTRV2 and pEASY-Blunt Simple-2.8Agrobacterium solution, soaked for 10 min, and dried with sterile filter paper. Stems were then transferred to the co-culture medium  and cultured in darkness for 2–3 d. Thereafter, they were transferred to the bud inductive-differentiation medium , and incubated at 25°C under a light intensity of 4,000 μmol/m2·s, and a photoperiod of 16 h/8 h. The medium was replaced at 2-week intervals. When adventitious buds grew to 2.0 cm, they were transferred to the rooting medium . When plantlets were formed, total leaf RNA was extracted, and positive transgenic plants were identified using PCR and q-PCR separately.Potato stems (2 cm long) were placed in the suspended 2.9P. infestans were decolorized in anhydrous ethanol by boiling for 2 to 2.5 h. Decolorized leaves were placed in 0.5% trypan blue solution (0.5 g trypan blue dissolved in 100 mL distilled H2O), stained for 10–15 min, washed twice with distilled water, and then treated with lactophenol water .Leaves infected with 2.10P. infestans were soaked overnight in 1 mg/mL 3,3′-Diaminobenzidine (DAB) dyeing solution . The leaves were decolorized by boiling for 2 h in absolute ethanol, then treated with lactophenol water , and photographed. The H2O2 content of P. infestans-inoculated transgenic plants was determined as described by Leaves infected with 2.11Activities of APX CAT, POD, and SOD enzymes were determined according to manufacturer’s instructions using the corresponding kits , purchased from Suzhou Keming Biotechnology Co., Ltd., . The infected leaf samples were collected from all treated plants. The data were analyzed using SPSS 16.0.2.120.1 g smples collected from potato leaves were cut into pieces, and grinded into powders in liquid nitrogen. The content of JA in potato was determined by plant JA ELISA Kit  .33.1StRab5b gene was amplified using Prime STAR HS DNA Polymerase (Takara). The fragment identified via agarose gel electrophoresis and the amplicon size was approximately 607 bp. To analyze the conserved domains of StRab5b, Rab 5b proteins isolated from rice, N. benthamiana, lotus, and Physcomitrella patens were retrieved from Genbank and used for multi-sequence analysis. The results suggested that StRab5b contained the basic general characteristics of Rab proteins, including RabF2 (83−87), RabF4  and RabF5 (131−135) domains, which are the conserved domains of the Rab proteins. Additionally, it also contained the GTP/GDP binding domain, phosphate binding loop G1 (39−46), G2 (79−80), and the coordination of GTP β and γ phosphate motifs G3 (88−91) and G5 (176−178), binding motifs NKAD (146−149) and ETSA (174−177)  with NbRab5b, whereas, it showed the lowest similarity (44%) with TaRab  at 108 hpi; it is much bigger than the lesion size on leave which transiently expressed StRab5b, indicating that transiently ectopic expression of StRab5b could enhance N. benthamiana resistance to P. infestans  did not differ extensively  , the highest H2O2 level of 0.156 μmol/g was detected in transgenic line 2 at 72 hpi, followed by other two transgenic lines 3 and 8 with values of 0.136 and 0.132 μmol/g, respectively. The value of H2O2 was significantly higher than that in control plants after inoculation. These results suggested that StRab5b positively regulates potato resistance through H2O2 accumulation.DAB staining was performed to detect whether H3.6StRab5b-transgenic leaves were substantially higher than those in control plants at different inoculation time points  that encode ROS scavenge enzymes were examined using qPCR after inoculation. The relative expression levels of four genes are consistent with the trend of enzyme activities. The maximum relative expression levels of APX1, CAT2, POD, and SOD genes were 5.3, 4.2, 6.2, and 7.5-fold, respectively.Four antioxidant enzyme activities  were monitored after inoculation. The results suggested that APX, CAT, POD, and SOD activities in e points Figure 63.7StRab5b-mediated resistance to P. infestans, several marker genes related to different signaling pathways were analyzed at different time points. The results suggested that the expression levels of LOX and JA content in three transgenic lines  were induced at 24 hpi and subsequently dropped at 48 hpi, followed by reaching the highest level at 72 hpi ; moreover, JA content in transgenic plants was significantly higher than that in EV. Whereas, both NPR1 and ACS showed a different induction pattern compared with the LOX gene. They reached the peak at 24 hpi, then decreasing gradually. However, if we compared to the expression of the three marker genes, the induction of LOX was much more dramatic than ACS or NPR1. The induction rate was 67.5-fold at 24 hpi and 141.0-fold at 72 hpi separately, indicating that JA was the dominant molecular signal, which was involved in StRab5b-mediated resistance against P. infestans  of the StRab5b was analyzed using multiple sequence alignment. The guanine-binding motif NKAD and the guanine-binding and dissociation motif ETSA were consistent with the expression of TaRab5b protein . The spe protein . TherefoStRac , TRV:17 (UDP-arabinose 4-epimerase), TRV:21 (2-oxoglutarate dehydrogenase), TRV:22 (Lipoxygenase), and TRV:48 (suberization-associated anionic peroxidase) from N. benthamiana facilitated the infection of P. infestans  is an important method for studying gene function and has been used routinely in thaliana . In the l leaves Figure 3thamiana . Consistnfestans . Howeverll death . Silencinfestans . Converst stress .4.32O2 was previously recognized as a toxic metabolite in plant cells. However, it has been known that H2O2 also mediates stimulus responses to plant cells as a signaling factor  pathways. The production of H2O2 in plants generally suffer abiotic stress through the POD pathway. The extracellular PH value temporarily increases, which can activate the peroxidase of cell wall, and reduces O2 to H2O2 . Conversely, the inhibition of RBOH reduced stress defense response and antioxidant enzyme activity   . In addiK plants . Brassin healing . Some re4.5OsRab11 in Arabidopsis enhances plant resistance to Pseudomonas syringae by inducing the expression of JA-responsive genes, rather than SA-responsive genes  proteins. Plant hormones SA, JA, and ET play major roles in the plant immune system . There ave genes . These rpression . SA and nfestans , and theesponses . In otheThe original contributions presented in the study are included in the article/ZT and ZZ conceived the experiments. JuZ designed and guided the experiments scheme. ZT, ZZ, LK and ML completed the experiments. ZT, XG, JY, JiZ and YF provided consumable materials/reagents/software and analyzed the data. ZT wrote the paper. All authors contributed to the article and approved the submitted version.


Microfluidic artificial lungs (μALs) are being investigated for their ability to closely mimic the size scale and cellular environment of natural lungs. Researchers have developed μALs with small artificial capillary diameters  and with large capillary diameters . However, no study has directly investigated the impact of capillary height on μAL properties. Here, we use Murray’s law and the Hagen-Poiseuille equation to design single-layer, small-scale μALs with capillary heights between 10 and 100 µm. Each µAL contained two blood channel types: capillaries for gas exchange; and distribution channels for delivering blood to/from capillaries. Three designs with capillary heights of 30, 60, and 100 µm were chosen for further modeling, implementation and testing with blood. Flow simulations were used to validate and ensure equal pressures. Designs were fabricated using soft lithography. Gas exchange and pressure drop were tested using whole bovine blood. All three designs exhibited similar pressure drops and gas exchange; however, the μAL with 60 µm tall capillaries had a significantly higher wall shear rate , smaller priming volume and smaller total blood contacting surface area than the 30 and 100 µm designs. Future μAL designs may need to consider the impact of capillary height when optimizing performance. Hollow fiber membrane (HFM) lungs are used in millions of cardiopulmonary bypass procedures each year and provide lung support to tens of thousands of patients with lung disease ,2. RecenTo overcome the drawbacks of HFM lungs, researchers have investigated using microfluidic technology to develop artificial lungs that more closely mimic their natural counterpart. These microfluidic artificial lungs (μALs) can exhibit µm-tall artificial capillaries, thereby increasing gas exchange efficiency and achieving physiologic blood flow paths, potentially increasing biocompatibility compared to current alternatives. To date, our and other research groups have demonstrated small-scale, single-layer two-dimensional (2D) µALs with record gas exchange efficiency ,11, biomc) between 10 and 100 μm are compared for a fixed set of performance specifications (rated blood flow and blood-side pressure drop). All designs contained blood distribution channels and artificial capillaries and were constrained to fit on a 6″ silicon wafer. Rated blood flow and total pressure drop were fixed at 0.8 mL/min and 50 mmHg for all designs. Surface area (gas exchange and blood contacting), blood priming volume in capillaries and distribution channels (Vprime) capillary wall shear rate and number of bifurcations were determined for each capillary height and compared. Blood flow was modeled in three designs  using CFD, implemented via soft lithography and tested in vitro with bovine blood to verify performance. A preliminary version of this work was accepted as a conference abstract and presented at the ASAIO 66th Annual Conference in June 2021 [In this work, μAL designs with capillary heights . 2-dimensional (2D) blood flow paths were designed in DraftSight . SolidWorks  was used to render 3-dimensional (3D) designs and run flow simulations (CFD). Silicon wafers were purchased from University Wafer  and Microchem SU-8 2035 and SU-8 2100 permanent epoxy negative photoresist were purchased from Kayaku Advanced Materials . Custom designed photomasks  were purchased from FineLine Imaging . Acetone, 2-isopropanol and SU-8 developer were purchased from Fisher Scientific . Dow Corning 3140 RTV silicone conformal coating and Dow Corning Sylgard 184 (PDMS) silicone elastomer base with curing agent were obtained from Ellsworth Adhesives . A Dino-Scope 2.0  was used for optical imaging. A MARCH AP-300 Plasma System  was used for plasma activation of PDMS surfaces for bonding. Silastic tubing, luer connectors, three-way stop cocks and Masterflex Digital Benchtop Gear Pump System were purchased from Cole Parmer . An i-STAT Handheld blood analyzer, ACTc cartridges and CG8+ cartridges were obtained from Abbott Point of Care . Bovine whole blood was obtained from Lampire Biological Laboratories . Pressure measurements were achieved using a Hewlett Packard M1094B pressure monitor , Philips Medizin Systeme M1006B , Transpac ICU Medical Hospira Reusable Cables  and Transpac disposable pressure transducers .−2 (43–260 s−1), and arteriole wall shear stress spans 10–70 dynes∙cm−2 (434–3038 s−1) [In this study, all μALs were designed to contain a blood flow layer, a non-porous gas-diffusion PDMS membrane and a gas flow layer . The Hagc,g for the target rated blood flow, where QR is defined as the highest blood flow rate at which an inlet blood oxygen saturation of 70% can be increased to 95% upon exiting from the outlet [c,g includes the portion of the capillaries that actively contributes to gas exchange and excludes distribution channels. Once Ac,g was determined, the target pressure drop and Equation (3) were used to solve for the number and length of artificial capillaries via Solver, a Microsoft Excel Add-in.Gas exchange and pressure drop calculations were used to determine the number and length of artificial capillaries necessary for each design (Equations (1)–(3)) ,34,38. Ec,g is the available capillary gas exchange area, SB,O2 is the average effective solubility of oxygen in blood and RD,O2 is the membrane’s effective resistance to diffusion. PO2B,i, PO2B,o and PO2G are the partial pressures of oxygen in the blood entering and exiting capillaries and in the sweep gas, respectively. In normal human blood, SB,O2 is a constant, at approximately 7.9 × 10−4 mL-O2∙mL-blood−1∙mmHg−1 [G was defined as 760 mmHg. PO2B,i and PO2B,o were defined as 36.4 mmHg and 79.2 mmHg, corresponding to SO2 values of 70% and 95%, respectively. Thus, RD,O2 was calculated to solve for Ac,g.In Equation (1), A1∙mmHg−1 ,40. A swM), channel height (H), membrane permeability to oxygen , effective diffusivity of oxygen in blood  and SB,O2. Devices were designed to contain 30 μm-thick membranes constructed from PDMS, which corresponds to a PM,O2 of 3.6 × 10−7 mL-O2∙cm−1∙min−1∙mmHg−1. The FDA Guidance for Cardiopulmonary Bypass Oxygenators 510(k) Submissions defines normal human blood as having a DB,O2 of 1.4 × 10−6 cm2∙s−1 [ cm2∙s−1 .Pressure drop for capillaries and distribution channels was determined using Equation (3), a combination of Poiseuille’s law and Kirchhoff’s voltage law , where Q1 bifurcates from its respective parent vessel with radii rp at an angle α. r2 is the radius of the parent vessel that continues on after the daughter channel branches off (H) were converted to channel width (W) and height (H) using Murray’s law describes how blood vessels scale and branch in the context of natural lungs; it was used here to determine how the distribution channels change in size at each bifurcation (Equations (4) and (5)) ,36,37. Mches off . Since tc = 30, 60, 100 μm) spanning the mathematically modeled range were selected for blood flow simulations in CFD. After which, the three designs were implemented via soft lithography and testing in vitro to verify performance and CFD simulations.Three designs corresponding to three artificial capillary heights  and one gas layer for designs with artificial capillary heights of 30, 60, or 100 μm. Capillaries were kept parallel to each other and distribution channels were curved to accommodate branching angles determined via Murray’s law (Equation (5)). Due to the extreme curving of the distribution channels in HTwo-dimensional designs from DraftSight were rendered into three-dimensional models in SolidWorks. Inlet volume flow and outlet pressure boundary conditions were assigned as 0.8 mL/min and 760 mmHg, respectively. Inlet flow was applied uniformly across the inlet distribution channel’s cross-sectional area. Outlet pressure was applied uniformly at the outlet distribution channel’s cross-sectional area.The Carreau model accounts for shear rate dependent viscosity , and wasBlood and gas molds were made from conventional photolithography and soft lithography techniques as follows. First, negative photoresist SU-8 2035 was spin-coated onto 6″ silicon wafers at speeds that would produce an artificial capillary height of 30, 60 or 100 μm. Next, two soft bakes, UV-exposure with a photomask, two post-exposure bakes and a development step were performed to pattern and cure the photoresist. Microchem’s data sheet was used as a starting point for spin speeds, bake temperatures, bake durations and UV-exposure durations , but adjTo form distribution channels, SU-8 2075 was applied atop the capillary layer by repeating previously described steps. Two layers of SU-8 2075 were necessary to achieve thicknesses greater than 200 µm. Alignment markers were used to ensure capillaries aligned with distribution channels. Specific processing times and settings are outlined in Devices were constructed using standard soft-lithography techniques, as has been demonstrated in previous works ,52,53,54c = 30 devices, so Hc = 30 devices were re-made and coated with polyethylene glycol (PEG) to ensure gas exchange and pressure drop data would be reflective of complete capillary filling. PEG coating followed our previously established methods [c = 60 and Hc = 100 devices were left uncoated, since their capillary beds perfused easily at all experimental flow rates.Initial tests showed incomplete capillary filling in uncoated H methods . Given tPrior to each experiment, devices were primed with normal saline. Devices were tested at 0.2, 0.6, 0.8 and 1.2 mL/min. An inlet blood gas was taken immediately before each device was tested to ensure blood was being sufficiently preconditioned. After any change in blood flow rate, blood was permitted to flow through the μAL until its blood volume had been replaced at least 2× before taking the next measurement. Experiments were performed in triplicate (3 devices) at blood flow rates of 0.6, 0.8, 1.0 and 1.2 mL/min, with pure oxygen sweep flowing at 4× the blood flow rate.p-value was less than the alpha value (0.05), data was further analyzed to detect which specific pairs of designs were statistically significant. Paired comparisons were made using post-hoc Tukey-Kramer and deemed significant given alpha = 0.05. All statistical analyses were performed in Microsoft Excel.Pressure drop and oxygen saturation data for each design at each flow rate were combined into means and standard deviations. A single factor analysis of variance (one-way ANOVA) test was used to assess for statistically significant differences between results from the three designs at each flow rate. If the one-way ANOVA b,c) and Vprime exhibited a maximum and capillary wall shear rate (τw) exhibited a minimum around Hc = 40 μm.Previously proven mathematical models were used to design μALs with varying capillary heights, but equal rated flow and pressure drop. Results are shown in c of 30, 60 and 100 μm were selected. Resulting channel dimensions for Hc = 30, 60 and 100, respectively, are: capillary widths of 120, 240 and 400 µm; capillary lengths of 0.195, 2.001 and 5.610 cm; distribution channel heights of 239, 210 and 241 µm; distribution channel widths between 27–957, 62–841 and 120–964 µm; distribution channel lengths of 9.144, 1.4623 and 0.700 cm. We achieved channel heights within 3% of intended dimensions. For the sake of brevity, individual branching angles are not included; however, branching angles for Hc = 30, 60 and 100 ranged from 85.2–54.0°, 79.0–70.9° and 73.7–54.0°, respectively. Implemented branching angles were within 1–12% of what was theorized using Murray’s Law (Equation (5)).Based on the results of the mathematical modeling, three designs were chosen for further modeling and implementation. Designs with capillary heights ≤ 25 μm were removed from consideration since their resulting designs would not fit on 6″ silicon wafers. To evenly represent the reduced range, designs with Hc = 30, 60 and 100 required gas exchange surface areas of 1.2, 2.1 and 3.1 cm2, respectively, to meet rated flow, pressure drop and wall shear rate goals  of each design are shown below . Revised designs were used for all subsequent steps.Initial CFD simulations of devices were run at the rated blood flow (0.8 mL/min) and resulted in a substantial range in pressure drops across the three designs (45.0–71.1 mmHg). Although the original pressure drop goal was 60 mmHg, the Hre drops . By revic = 100, but a Level 4 global mesh was necessary in Hc = 60 and Hc = 30 to capture shear rate details in capillary height-width cross-sections. A Level 4 local mesh provided no more insight than Level 5 in all designs. Ultimately, this meant computational domains for Hc = 30, 60 and 100 had 845,767, 1,226,957 and 1,723,605 fluid cells, respectively.Meshing in each design was systematically refined to maximize the precision of results while minimizing simulation time. Optimal mesh levels in SolidWorks were determined by increasing the mesh level stepwise until average velocity, wall shear stress and total pressure drop changed by <1%. A Level 3 global mesh was appropriate for H−1 in Hc = 30, 60 and 100, respectively .One-way ANOVA of pressure drop data indicated a significant difference between one or more design types at each flow rate. 2 mL/min . This mac = 100 at 0.6 mL/min. The one-way ANOVA test for comparison at 0.6 mL/min resulted in p = 0.032; a post-hoc Tukey-Kramer test was performed, revealing significant differences between only Hc = 60 and Hc = 100 , as is similarly notable in Hc = 100’s capillaries, while Hc = 60’s capillaries appear green (1041–2039 s−1)  . This ag039 s−1) , Table 2c = 30’s shear rate cross-section appears less symmetric than the two other designs  and achieving the expected parabolic shear pattern in all three designs, H designs ; this isc = 30 may be due to the increased wettability caused by PEG coating. We have found in previous work that a hydrophilic surface reduces pressure drop in small diameter flow channels [c = 60 and Hc = 100 were not significantly different  given fixed parameters; however, further validation with in vitro tests using human blood would help elucidate the implications of capillary height on biocompatibility.In this work, three μALs were developed with differing channel dimensions but equivalent, physiologic parameters, such as rated flow and pressure drop. In CFD simulations, the design containing 60 µm-tall capillaries required the lowest total blood contacting surface area and priming volume but subjected blood to the highest wall shear rate. In vitro experiments using bovine whole blood revealed that gas exchange was similar between all designs, but the two designs with larger capillaries  consistently produced higher pressure drops than the design with smaller capillaries , although this may have been due to the hydrophilic surface coating on the design containing 30 µm tall capillaries. Overall, results regarding capillary size and performance may be valuable for guiding future work which manipulates blood flow networks to improve biocompatibility of μALs.


Renal cell carcinoma (RCC) is one of the most life-threatening urological neoplasms. The tumor microenvironment comprising immune cell infiltration is a key factor for treatment response and survival of RCC patients. In addition, several studies focused on the involvement of the microbiome in tumor progression via the secretion of metabolic by-products such as succinate. In this study, we have highlighted the potential role of the succinate receptor, SUCNR1, in modulating the tumor microenvironment in the RCC subtypes. Our data displayed a distinct association of SUCNR1 with the microbiome signature, tumor immune infiltrates, and immunomodulators in two different RCC subtypes. This correlation could have potentially contributed to the different survival outcomes of the RCC patients. Thus, SUCNR1 may serve as a promising prognostic factor that might help in improving therapeutic interventions.p = 5.797 × 10−5) in KIRC patients albeit a poor prognostic prediction in KIRP patients (p = 1.9282 × 10−3). Intriguingly, SUCNR1 was mainly correlated to immunomodulators and diverse immune infiltrates in KIRP. Additionally, the SUCNR1 was mostly associated with a repertoire of microbes including beneficial bacteria that likely influenced a better disease-specific survival rate in KIRC. Our findings illustrate a significant novel subtype-specific role of SUCNR1 in RCC which potentially modulates tumor immune infiltration and microbiome signature, hence altering the prognosis of cancer patients.The succinate receptor, SUCNR1, has been attributed to tumor progression, metastasis, and immune response modulation upon its activation via the oncometabolite succinate. Nonetheless, little is known about the prognostic relevance of SUCNR1 and its association with tumor immune infiltrates and microbiota in renal cell carcinoma (RCC). Herein, publicly available platforms including Human Protein Atlas, cBioPortal, TIMER2.0, and TISIDB were utilized to depict a divergent implication of SUCNR1 in the immune microenvironment of clear cell RCC (KIRC) and papillary RCC (KIRP); the two major subtypes of RCC. Our results showed that the SUCNR1 expression level was augmented in RCC compared to other solid cancers, yet with opposite survival rate predictions in RCC subtypes. Consequently, a higher expression level of SUCNR1 was associated with a good disease-specific survival rate ( Renal cell carcinoma (RCC) is a heterogeneous life-threatening malignancy that originates from kidney tubular epithelial cells and is the most common form of kidney cancer. RCC is prevalent among men and women and incidences have been increasing over the past decades ,2,3. BasSuccinate receptor hereafter refer to as SUCNR1 is a G-protein-coupled receptor (GPR91) related to the P2Y purinoreceptors family, and is activated by a Krebs cycle intermediate metabolite, succinate . Until nMoreover, SUCNR1 is also linked to cancer and inflammatory pathologies ,22,23,24Recently, variation of immune cell infiltrates in the microenvironment of RCC subtypes has been reported  whereby,The Human Protein Atlas (HPA)  was utilp- and q-values were presented in p-values. The correlation between SUCNR1 mRNA expression and microbiota signature was analyzed using GraphPadTM software . The cBioPortal platform was also used to generate Kaplan–Meier curves of high or low SUCNR1/ microbiota expression levels. Whereby clear cell and papillary RCC patients were divided into 4 expressing groups of low SUCNR1/low microbiota, high SUCNR1/low microbiota, low SUCNR1/high microbiota, and high SUCNR1/high microbiota.The cBioPortal ,39 platfTM software  and the logrank test. The correlation of SUCNR1 expression with an abundance of immune infiltrates and immunomodulators expression was evaluated with the TISIDB platform using Spearman’s correlation analysis. The differential expression of SUCNR1 between normal and tumor tissues across TCGA tumors was determined by TIMER2.0 using the Wilcoxon test. The difference in microbial expression between low and high SUCNR1-expressing groups was assessed by cBioPortal using Student’s t-test or Benjamini-Hochberg procedure and presented as mean ± SD. The correlation between SUCNR1 and microbial expression was analyzed via GraphPadTM software  using Spearman’s correlation analysis. Statistical significance was reported as follows: * for p-value < 0.05; ** for p-value < 0.01; *** or **** for p-value <0.001.The distribution of low and high SUCNR1-expressing patient groups to renal tumor type and stages was assessed using the cBioPortal platform using the Chi-squared test. The Kaplan–Meier curves presenting disease-specific survival rates were estimated using cBioPortal and the logrank test. The Kaplan–Meier curves of high or low SUCNR1/microbiota expression levels were studied using GraphPadp < 0.001)  . Moreove< 0.001) . This imp < 10 × 10−10), whereas 60.86% of group A included KIRP patients (p < 10 × 10−10) (p = 5.797 × 10−5) (p = 0.018) (p = 1.9282 × 10−3) (p = 8.932 × 10−3)  A. This f × 10−5) B. Additi= 0.018) D. Surpri × 10−3) C leading × 10−3) E. This ip = 2.15 × 10−4), eosinophils , mast cells , and neutrophils  (p = 0.0168), Th2 cells , and regulatory T cells  , CD56bright NK cells , and CD56dim NK cells   cells  A. In add × 10−6) B. Howeve 0.0351) C.p = 1.36 × 10−11), NKT cells , CD56bright NK cells , CD56dim NK cells , MDSC , activated dendritic cells (DC) , plasmacytoid DC , mast cells , macrophages , monocytes , and eosinophils  , activated CD8+ T cells , Th1 cells , Th2 cells , gamma delta T cells , regulatory T cells , T follicular helper cells , and activated B cells   A, along × 10−11) B.Collectively, these results showed that SUCNR1 is associated with a variety of immune cell subsets in KIRP compared to KIRC. This further emphasized the divergent role that SUCNR1 specifically plays in altering the tumor immune infiltration dependent on the RCC subtype.p = 2.2 × 10−9), IL10 , PDCD1LG2 , TGFBR , CTLA4 , and LAG3  (p = 2.57 × 10−6), IL6R , CD28 , CD27 , and CXCR4   A. Regard 0.0102) B.p < 0.001) associated with a wide repertoire of immunoinhibitors including; CD244 (ρ = 0.293), CD274 (ρ = 0.576), CD96 (ρ = 0.37), CSF1R (ρ = 0.351), CTLA4 (ρ = 0.171), IDO1 (ρ = 0.229), IL10 (ρ = 0.356), LAG3 (ρ = 0.235), PDCD1 (ρ = 0.145), PDCD1LG2 (ρ = 0.59), TGFB1 (ρ = 0.305), TGFBR1 (ρ = 0.543), and TIGIT (ρ = 0.411)  C. In add= 0.411) D.These observations highlight the involvement of SUCNR1 in the tumor immunity of RCC subtypes, and of KIRP to more extent. Accordingly, the consequential correlation between SUCNR1 expression and diverse immunomodulators in KIRP, emphasizes the role of the receptor in immune-specific modulation and potential survival outcome.Recently, tumor microbiota has been the focus of analyzing the function and induction of cancer patients’ immune responses. We assessed the relation between the expression of SUCNR1 and the microbiome in RCC using cBioPortal datasets. The KIRC and KIRP patients were divided into groups A and B based on low and high SUCNR1 expression levels, respectively. The significantly different microbiome present in both groups and their mean of expression (log RNA Seq CPM) are listed in Indibacter, Candidatus nitrosopelagicus, Lachnoclostridium, Desulfotalea, and Flavonifractor. To investigate the role of these bacteria in the prognosis of KIRC and KIRP patients, the patients were divided according to the median microbiome expression levels. Therefore, groups A and B reflect low and high levels of the corresponding bacteria, respectively. Intriguingly, only two out of the five common genera, Candidatus nitrosopelagicus (p = 4.531 × 10−6) and Indibacter (p = 3.533 × 10−4), were associated with good disease-specific survival prediction in KIRC patients     C, and In 0.0001) D. Moreov 0.0001) C,D. ThesAnoxybacillus and Selenomonas genera, which were high in SUCNR1 group A  and Selenomonas  were negatively associated with SUCNR1 expression (p < 0.001)  B,C. TherApibacter, that was high in SUCNR1 group B (p = 0.049)    A. Moreov 0.0001) B. Patien × 10−3) B. As sucThe multifaceted receptor, SUCNR1, has been attributed to kidney physiopathology via stimulation of the local and systemic renin–angiotensin system, development of metabolic syndrome, and hypertension ,42. HoweHere we first illustrate a decrease in SUCNR1 expression in RCC patients compared to healthy individuals. Even though it was associated with stage IV cancer and the worst prognosis in KIRP patients. The high expression of SUCNR1 in normal kidney tissue might be due to its pivotal function as a physiological sensor, such as maintenance of angiotensin II and renin levels, sodium reabsorption by collecting duct, and proper proximal tubule . Hence, The succinate–SUCNR1 axis and its downstream signaling are not fully elucidated in cancer . In pheo+ T cells, Th1 cells, Th2 cells, γδ T cells, Treg cells, Tfh cells, activated B cells, NK cells, NKT cells, CD56bright NK cells, and CD56dim NK cells. Along with MDSC, activated DC, pDC, mast cells, macrophages, monocytes, and eosinophils. However, even with the association of SUCNR1 with several effector cells such as certain T cells, NK, and DC subsets, the outcome in KIRP was unfavorable. This might be explained by SUCNR1-related regulatory cells  in the infiltrate [Comparably, our data illustrated that high expression of SUCNR1 in KIRP was closely related to lymphocyte infiltration including, activated CD8filtrate  and poss+ T cells present in RCC tumors were shown to be non-responsive upon ex vivo stimulation, lack cytolytic activity, granule mobilization, and cytokine production [+ T cell proliferation via PD-1 ligand (PD-L1) [Indeed, NK cells and CD8oduction ,51. Furtoduction . This in (PD-L1) . Moreove (PD-L1) . Mast ce (PD-L1) . Additio (PD-L1) . Regardi (PD-L1)  and asso (PD-L1) .Our findings also revealed the association of SUCNR1 with fewer infiltrated immune cells including NK cells, eosinophils, mast cells, neutrophils, Th1, Th2, and regulatory T cells in KIRC. This difference in SUCNR1-related immune infiltration between the two RCC subtypes might be due to variability in initial immune cell abundance . Further+ T cell activity [Our study positively associated SUCNR1 with higher diversity of immune checkpoints in KIRP compared to KIRC. Those immune inhibitors and stimulators are well recognized as pro-tumorigenic. For instance, cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and PD-1 are immune inhibitors that were associated with the worst overall survival in RCC patients . Moreoveactivity . To subsactivity . One of activity . CXCR4/Cactivity . Hence, There are several probable sources that cause succinate accumulation during pathogenesis including tumor cells, inflammation, and microbiome . InteresCandidatus nitrosopelagicus and Indibacter belonging to the phyla Thaumarchaeota and Bacteroidetes, respectively, were common in KIRP and KIRC patients with high SUCNR1expression levels.Gut microbiota is a major source of succinate and was suggested to induce effects on distant organs including the kidneys . HealthyAnoxybacillus and Selenomonas belonging to the phylum Firmicutes, which were more associated with low SUCNR1 levels, had the worst prognosis in KIRC. As such, the good outcome related to high SUCNR1 expression in KIRC might be related to the presence of beneficial bacteria. This was emphasized by the potential presence of bad bacteria in KIRC tissue in case of receptor under-expression. Additionally, the genus Apibacter belonging to the phylum Bacteroidetes was associated with the worst outcome in KIRP when SUCNR1 expression was high. This further explains the relation of SUCNR1 with bad prognosis in KIRP. The majority of bacterial species classified as succinate-producers or succinate-consumers belong to the phyla Bacteroidetes and Firmicutes [B. vulgatus, was increased in IBD patients. Whereas, the succinate consumer P. succinatutens was decreased [However, the two bacteria were only related to a better outcome in KIRC patients, especially when SUCNR1 expression is high. Nonetheless, the genera rmicutes . In inflecreased . TherefoIn this in silico study we have unraveled SUCNR1 probable function in altering the tumor microenvironment in RCC patients, using correlation analysis of data displayed in public databases. Our study broadens the perception of the SUCNR1 role, for the first time, as not only cell-specific but also as tumor-specific. The findings of this study highlighted the receptor link to immune infiltration and specific microbiome profile. The mechanisms behind this probable divergent role of SUCNR1 in orchestrating immune infiltrates and microbiome population in RCC should be immensely addressed to aid the development of targeted therapies. This study provides the conceptual basis for the involvement of SUCNR1 in the tumor microenvironment, which may be applied to different types of cancers.


Tubulin alpha 1b (TUBA1B) is an important microtubule isoform that is involved in the formation of the cytoskeleton. The objective of our study was to explore the potential of TUBA1B in predicting the prognosis of HCC and patients’ response to immunotherapy. Raw data was extracted from TCGA and GEO databases, and then HCCDB, TIMER, HPA, and GEPIA websites, as well as R software, were used to perform bioinformatics analysis to investigate the potential of TUBA1B as a prognostic and immunotherapeutic marker for hepatocellular carcinoma (HCC). We found that both TUBA1B mRNA and protein were highly expressed in HCC. TUBA1B was proved to be an independent prognostic predictor of HCC. Additionally, TUBA1B expression was associated with the infiltration of several immune cells in HCC. Moreover, TUBA1B was coexpressed with immune-related genes and immune checkpoints. Patients expressing high TUBA1B responded better to immune checkpoint inhibitor (ICI) therapy. GO and KEGG analyses revealed that TUBA1B may be involved in the processes of cell cycle, spliceosome, and DNA replication. In conclusion, TUBA1B is expected to be a prognostic and immunotherapeutic marker for HCC. Primary liver cancer, which accounts for 6% of all cancers and 9% of all death from cancer worldwide, is a cancer in urgent need of further research. Hepatocellular carcinoma (HCC) accounts for about three quarters of primary liver cancers ,2,3. The+ T cell [Tubulin is an essential cytoskeletal component that comes in five different forms, α-, β-, γ-, δ-, and ε-tubulin. α- and β-tubulin heterodimers constitute microtubules, which participate in cell motility, adhesion, generation, and division. Tubulin alpha 1b (TUBA1B) is an important isoform of α-tubulin ,6. It ha+ T cell . In addi+ T cell . A model+ T cell . OverallIt is well known that tumor-infiltrating immune cells (TIICs) are essential components of the tumor microenvironment (TME) and that they are closely linked to the development of tumors . RecentlIn our study, we first evaluated the differential expression of TUBA1B in HCC tissues and normal tissues. Next, we comprehensively assessed the relationship between TUBA1B expression and the clinicopathology and prognosis of HCC using multiple bioinformatic approaches. We also analyzed the correlation between TUBA1B and TME, TIIC, immune checkpoints, and immune-related genes. The protein–protein interaction (PPI) information of TUBA1B was detected, and the potential functions of TUBA1B were explored by gene enrichment analysis. Our findings provide novel perspectives on the role of TUBA1B and offer new targets for the diagnosis, therapy, and prognosis of HCC.https://xena.ucsc.edu/, originally from TCGA) (accessed on 14 January 2022) website. TUBA1B expression data were downloaded from the TCGA (https://tcga.xenahubs.net) (accessed on 14 January 2022) database and processed and analyzed by Perl and R software. We used the “Wilcox.test” method to assess the differential mRNA expression of TUBA1B in HCC and normal tissues. False discovery rate (FDR) values < 0.05 were used as cut-off values. The boxplot was plotted with the “ggpubr” R package. Moreover, we also extracted the expression dataset from the GEO (http://www.ncbi.nlm.nih.gov/geo/) (accessed on 14 January 2022) [http://gepia.cancer-pku.cn/index.html) (accessed on 15 January 2022), a website tool for normal and cancer gene expression profiling and interactive analyses using data from TCGA and GTEx [http://lifeome.net/database/hccdb.html) (accessed on 15 January 2022), an online resource dedicated to assisting in the exploration of HCC gene expression [https://cistrome.shinyapps.io/timer/) (accessed on 17 January 2022), a database for TIICs analysis [TUBA1B expression between normal and HCC tissues.Cancer-related RNA sequences, survival data, and clinicopathology for HCC were obtained from the UCSC Xena website (ry 2022) , includiand GTEx , to furtpression , and TIManalysis , were ushttps://www.proteinatlas.org/) database (accessed on 19 January 2022).Immunohistochemical images of the TUBA1B protein expression in HCC and normal tissues were investigated immediately afterward to assess the differential TUBA1B protein expression in the human protein atlas , progression-free survival (PFS), and disease-free survival (DFS) were employed to clarify the association of TUBA1B and the HCC patients’ prognosis. We used Kaplan-Meier (KM) and log-rank tests to perform survival analysis, with survival curves plotted using the R packages “survivor” and “survminer”. Cox analysis was carried out using the “survival” package, and clinicopathological correlations were conducted using the R packages “ggpubr” and “limma”.+ T cells, macrophages, and CD8+ T cells in HCC. Additionally, the R packages “limma” and “estimate” were used to calculate stromal and immune scores using the ESTIMATE algorithm, a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumor samples [The TIMER database was used to investigate the correlation of TUBA1B with TIIC, including neutrophils, B cells, dendritic cells, CD4 samples . Based o samples . We usedSubsequently, we selected PDCD1, CTLA4, SIGLEC15, HAVCR2, TIGIT, CD274, PDCD1LG2, and LAG3 as immune checkpoint-associated transcripts and extracted their expression data. In addition, we analyzed the expression data of chemokines, chemokine receptors, major histocompatibility complexes (MHC), and immunoinhibitory and immunostimulatory genes. R packages “ggplot2”, “immuneeconv”, “pheatmap”, “limma”, ”RColorBrewer”, and “reshape2” were used to evaluate the coexpression of TUBA1B with these genes. Moreover, we applied the tumor immune dysfunction and exclusion (TIDE) algorithm to evaluate the patients’ response to ICI therapy.http://genemania.org/search/) (accessed on 2 February 2022) [https://cn.string-db.org/) (accessed on 2 February 2022) websites to further investigate the functions and mechanisms of TUBA1B.The TUBA1B PPI information were constructed by GeneMANIA (http://www.linkedomics.org/) (accessed on 3 February 2022), a web server for analyzing multi-omics data in 32 types of cancer [GO functional annotation and KEGG enrichment pathway analysis of TUBA1B were performed in LinkedOmics . Gene expression information was processed for normalization by log2 transformation. The cut off was median expression to define the high or low expression. Tumor and normal tissues were compared using a two-group p < 0.001) (p < 0.0001) (p = 0.0022) and GSE84402 (p = 0.0016) datasets from GEO also revealed that TUBA1B was significantly overexpressed in HCC.To obtain convincing results, we investigated differential TUBA1B mRNA expression in normal and HCC tissues using three databases and R software. TIMER online tool revealed that TUBA1B was upregulated in liver hepatocellular carcinoma (LIHC) (< 0.001) A. We the< 0.001) B. Higher< 0.001) C. Additi 0.0001) D. UltimaThe immunohistochemical images of TUBA1B in HCC from HPA also confirmed that TUBA1B protein was also upregulated in HCC tissues. Immunohistochemistry (IHC) staining was weaker in normal tissues than in LIHC tissues G.p = 0.00678). DFS was also distinctly shorter in the high TUBA1B expression group than the group of low TUBA1B expression (p = 0.0377). Moreover, HCC patients with low TUBA1B expression had longer DSS (p = 0.012) and PFS (p = 0.00211). Overall, high TUBA1B expression predicts a poor prognosis for HCC patients.The correlation of TUBA1B expression with OS, DFS, DSS, and PFI was further investigated to explore the potential of TUBA1B as a prognostic marker for HCC. KM survival curves showed that high TUBA1B expression led to a shorter OS in patients with HCC (p = 0.0066)  (p = 0.00304) (p = 0.00356) (We further investigated the relationship of TUBA1B expression with tumor stage and age. We found TUBA1B was more overexpressed in stage Ⅲ than in stage Ⅰ of LIHC ( 0.0066) A. Notabl 0.0066) B. Next, 0.00066) C were in0.00304) D and pTN0.00356) D also ha0.00356) E. The hi0.00356) F. Taken r = 0.507, p < 0.001), CD4+ T cells , CD8+ T cells , neutrophils , dendritic cells , and macrophages  , macrophages M0 , activated memory CD4 T cells , and T cells follicular helper . In contrast, macrophages M2 , resting mast cells , activated natural killer (NK) cells , and T cells gamma delta  infiltration was negatively associated with TUBA1B expression.TIICs are important components of TME and have a significant influence on cancer progression. Hence, we first explored the relationship of TUBA1B with tumor purity and major TIICs. The results in the TIMER database showed that high TUBA1B expression promoted the infiltration of B cells (< 0.001) A. Next, p = 1.06 × 10−2), LAG3 (p = 1.49 × 10−2), PDCD1 (p = 3.61 × 10−6), CTLA4 (p = 9.31 × 10−7), TIGIT (p = 2.28 × 10−7), HAVCR2 (p = 6.96 × 10−11), CD274 (p = 7.21 × 10−10), and PDCD1LG2 (p = 1.94 × 10−5). In addition, we used the GEPIA 2.0 database to analyze the relationship of TUBA1B expression with these immune checkpoint genes in LIHC and observed that TUBA1B expression was positively correlated with the expression of SIGLEC15 , LAG3 , PDCD1 , CTAL4 , TIGIT , HAVCR2 , CD274 , and PDCD1LG2 . Additionally, we explored the relationship of TUBA1B expression with the intensity of response to immunotherapy in HCC patients. The results indicated that the TUBA1B high expression group had a higher score of TIDE, which predicted a better response to immunotherapy (p = 0.00033).Subsequently, we explored the relationship between TUBA1B expression and immune checkpoints. We found that a high expression of TUBA1B was associated with high immune checkpoint genes expression A,B, inclThe correlation between TUBA1B and immune-related genes expression was then explored. The results showed that TUBA1B was coexpressed with almost all chemokine, chemokine receptor, MHC, and immunoinhibitory and immunostimulatory genes in HCC. TUBA1B was also highly correlated with immune-related genes in many other cancers, including pancreatic adenocarcinoma (PAAD), ovarian serous cystadenocarcinoma (OV), thyroid carcinoma (THCA), kidney renal clear cell carcinoma (KIPAN + KIRC + KIRP), and prostate adenocarcinoma (PRAD), suggesting that TUBA1B may affect the immune microenvironment in these cancers .We used GeneMANIA and STRING databases to construct PPI networks for TUBA1B. The PPI network from GeneMANIA indicated that TUBA1B was coexpressed with CCT6A, CCT3, CCT7, TUBA1A, TUBB4B, TUBA1B, TUBB6, SIRT2, DNAJA1, and so on A. MoreovNext, we predicted the potential function of TUBA1B in HCC using the LinkedOmics database. GO analysis of biological process C indicatAs the most common fatal malignancy worldwide and the only one of the top five deadliest cancers that increases in percentage each year , liver cWe first investigated the differential TUBA1B expression in normal and HCC tissues. We found that both mRNA and protein of TUBA1B were notably overexpressed in HCC. Next, we explored the correlation between TUBA1B and the prognosis of HCC, and observed high probability of OS, DFS, DSS, and PFS in HCC in TUBA1B-low groups, indicating that high TUBA1B expression predicted a poor prognosis in HCC. Moreover, the role of TUBA1B in the clinicopathology of HCC was confirmed. The results show that TUBA1B was notably higher in stage Ⅲ than stage Ⅰ in HCC. Additionally, the univariate and multivariate Cox analysis demonstrated that TUBA1B was an independent prognostic factor for LIHC. We further constructed a nomogram using TUBA1B and pTNM_stage and achieved good results in predicting 1-, 3-, and 5-year OS in HCC patients. Overall, TUBA1B was a potential prognostic biomarker for HCC.+ T cells, CD8+ T cells, macrophages, dendritic cells, and neutrophils. We further analyzed the effect of TUBA1B on different immune cell subtypes and found that TUBA1B has different effects on different subtypes of the same cell type. Various published reports have declared that CD8+ T cells and CD4+ T cells play an opposing pro and anticancer role, and their effects are suppressed by Treg cells. The role of B cells in HCC remains controversial [Subsequently, our research explored the role of TUBA1B in tumor immunity for the first time. The findings showed that TUBA1B was closely correlated with TIIC in HCC and was positively related to infiltration of B cells, CD4oversial . Macrophoversial . Moreoveoversial . Dendritoversial . The mecNext, we investigated the correlation of TUBA1B expression with immune checkpoints, immune-related genes, and patients’ response to immunotherapy. The results show that TUBA1B was positively associated with various immune checkpoint genes expression, including SIGLEC15, LAG3, PDCD1, HAVCR2, TIGIT, CTLA4, PDCD1LG2, and CD274. Because overexpression of immune checkpoint genes is correlated with T-cell depletion and poor outcome of patients, this partly explains the carcinogenic role of TUBA1B. We also found that the TUBA1B high expression group had a higher score of TIDE, suggesting that outcomes of ICI treatment are better in patients with high TUBA1B expression. Moreover, TUBA1B was coexpressed with almost all chemokine, chemokine receptor, MHC, immunoinhibitory and immunostimulatory genes in LIHC, and it was also highly correlated with immune-related genes in many other cancers. Overall, TUBA1B has an essential role in cancer immunity.Finally, we investigated genes and pathways associated with TUBA1B to further explore the underlying role of TUBA1B. We found that TUBA1B was coexpressed with genes of the CCT gene family and genes of tubulin alpha and tubulin beta. As CCT genes play a prominent role in the folding of many important proteins during cell division, it is emerging as a key molecule in mitosis . TubulinIn conclusion, our study confirms that TUBA1B expression is elevated in HCC and high TUBA1B expression is correlated with a poor outcome in patients with HCC. In addition, TUBA1B is associated with immune cell infiltration, immune checkpoint genes expression, and immune-related genes expression in HCC. Patients with high TUBA1B expression responded better to ICI treatment. Additionally, TUBA1B may act through the regulation of the cytoskeleton as well as mitosis, and it is involved in a variety of important pathways. TUBA1B has great potential as a prognostic and immunotherapeutic marker for HCC.


The synthetase 3β-hydroxysterol-Δ24 reductase (DHCR24) is a key regulator involved in cholesterol synthesis and homeostasis. A growing body of evidence indicates that DHCR24 is downregulated in the brain of various models of Alzheimer’s disease (AD), such as astrocytes isolated from AD mice. For the past decades, astrocytic tau pathology has been found in AD patients, while the origin of phosphorylated tau in astrocytes remains unknown. A previous study suggests that downregulation of DHCR24 is associated with neuronal tau hyperphosphorylation. Herein, the present study is to explore whether DHCR24 deficiency can also affect tau phosphorylation in astrocytes. Here, we showed that DHCR24 knockdown could induce tau hyperphosphorylation at Thr181, Ser199, Thr231, Ser262, and Ser396 sites in C8D1A astrocytes. Meanwhile, we found that DHCR24-silencing cells had reduced the level of free cholesterol in the plasma membrane and intracellular organelles, as well as cholesterol esters. Furthermore, reduced cellular cholesterol level caused a decreased level of the caveolae-associated protein, cavin1, which disrupted lipid rafts/caveolae and activated rafts/caveolae-dependent Ras/MEK/ERK signaling pathway. In contrast, overexpression of DHCR24 prevented the overactivation of Ras/MEK/ERK signaling by increasing cellular cholesterol content, therefore decreasing tau hyperphosphorylation in C8D1A astrocytes. Herein, we firstly found that DHCR24 knockdown can lead to tau hyperphosphorylation in the astrocyte itself by activating lipid raft-dependent Ras/MEK/ERK signaling, which might contribute to the pathogenesis of AD and other degenerative tauopathies. Among multiple pathological changes that may contribute to the pathogenesis of Alzheimer’s disease (AD), tau pathology is of a central role. For decades, microtubule-associated protein tau has been viewed to be mainly expressed in neurons. However, it is noteworthy that mRNA and the protein of tau can also be detected in astrocytes, though in a less-abundant level –5. RecenDHCR24, also named seladin-1, is a crucial enzyme regulating cholesterol biosynthesis. In the post-lanosterol pathway of cholesterol synthesis, the final step in the Bloch pathway or the first step in the Kandutsch-Russell pathway is catalyzed by the DHCR24 , 17. In Cholesterol has been found to be a major component of the plasma membrane and lipid rafts/caveolae. As an indispensable structural lipid, cholesterol loss could lead to the membrane disorganization and disruption of membrane lipid rafts, resulting in the alterations of lipid raft-dependent cell signaling, such as PI3-K/Akt/GSK3β and MAPK/ERK , 32. Mea, HRP-conjugated Affinipure Goat Anti-Mouse IgG (H + L).DHCR24 , Tau5 , Phospho-Tau (Thr181) , Phospho-Tau (Ser199) , Phospho-Tau (Ser396) , Actin , cavin1 , Phospho-Tau (Ser262), Phospho-Tau (Thr231), Ras(27H5), p44/42 MAPK (ERK1/2) (137F5), Phospho-p44/42 MAPK (Erk1/2)(Thr202/Tyr204), MEK1/2 (D1A5), Phospho-MEK1/2 (Ser217/221), U0126 , Propidium Iodide , Methyl-β-cyclodextrin , Filipin III , Oil Red O , HRP-conjugated Affinipure Goat Anti-Rabbit IgG(H + L)2 in the incubator.The C8D1A cell line was generated by Alliot F and Pessac B in 1984 and was used for the in vitro study of astrocytes . These cMouse DHCR24 cDNA in pLVX-Puro vector (pLvx-Puro-DHCR24) and empty pLVX-Puro vector  were cloned into lentivirus transfer (HIV) plasmid and packed into third-generation self-inactivating lentiviral particles in 293 T cells. Transfections were performed using LipoD293  following the manufacturer’s instruction. The viral supernatant was collected at 24, 48, and 72 h after transfection and the cell debris was removed by centrifuging at 1000 g for 5 min. The viral supernatant was stored at − 80 ℃ before infecting cells for DHCR24 overexpression. We obtained the DHCR24-shRNA lentivirus from Hanbio Biotechnology (China). The ShRNA target sequence is as follows: (5′- GCGAGGAATTCTGGGAGATGTTCGA -3′).For lentiviral transduction, cells were seeded on a 6‐well plate for 24 h to reach 50% confluence before infection. DHCR24-cDNA, DHCR24-shRNA-containing lentivirus, and their control lentiviral particles were used to infect cells at multiplicity of infection (MOI) 25. The culture medium was added with 10 ug/mL hexadimethrine bromide for optimal infection and was removed after a 24-h culture. After 72 h, a medium containing 2 μg/mL puromycin was used for cell culture to kill noninfected cells. Western blot and quantitative real-time polymerase chain reaction (qPCR) were performed to confirm whether DHCR24 knock-in and knockdown cell lines were successfully established.Total protein lysates for Western blot were prepared by lysing the cells using SDS lysis buffer  supplemented with a protease and phosphatase inhibitor. The lysates were centrifuged at 12,000 × g, 4 ℃ for 15 min. We then used a BCA protein assay kit  to verify protein concentrations, and protein (20 μg per lane) was loaded into 10% SDS‐PAGE gel and subsequently transferred onto a PVDF membrane . After that, the PVDF membrane was blocked in 5% non-fat milk at room temperature (RT) for 1 h. The membranes then were incubated with primary antibodies at 4 ℃ overnight. After incubation with species-specific horseradish peroxidase (HRP)-linked secondary antibodies for 1 h at RT, the proteins were detected using enhanced chemiluminescence substrates (Millipore) and Chemiluminescent  and analyzed via Tannon 4600 Chemiluminescence Image Analysis System version 3.0 .Cells were seeded on a 12-well plate to reach 50% confluence. All the following procedures were performed at RT unless otherwise noted. Cells were at first fixed in 4% paraformaldehyde (PFA) for 30 min and permeabilized in 0.5% Triton X-100 for 20 min. Then, cells were incubated for 1 h in the blocking solution . Afterwards, cells were stained with Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (D13.14.4E)  at + 4 °C overnight, followed by staining with anti-rabbit Alexa594  secondary antibody for 1 h. After three times washes in PBS for 10 min, cell nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI)  for 5 min. Eventually, cells were imaged using a fluorescent microscope .The staining of Filipin III was conducted according to a previous study . BrieflyBriefly, cells were plated on a 12-well plate to reach 50% confluence for preparation, then the culture medium was removed and cells were washed with (PBS). Cells then were fixed in 4% PFA for 30, washed with 60% isopropyl alcohol for 10 s, and stained with ORO working solution (Sigma) for 15 min. After two times of washing with 60% isopropyl alcohol and PBS, the nuclei were stained with hematoxylin for 2 min. Cells were imaged using a light microscope . All of these procedures were performed at RT.The quantification of cellular cholesterol was conducted using an Amplex Red cholesterol assay kit  according to the manufacturer’s protocol and a previous study , 38. BriTotal RNA from cells was extracted using RNAiso Plus Kit ; reverse transcription was carried out using PrimeScript™ RT Master Mix ; and qPCR was performed with TB Green® Premix Ex Taq™ , all following the manufacturer’s instruction. The mRNA of the target gene was quantified in three independent cell cultures. And the value of threshold cycle (Ct) was acquired by Sequence Detection Software V1.4 . The comparative ΔΔCt method was used to analyze ACTIN-normalized expression levels of the target mRNA. Primers of actin were obtained from Sangon, China (B661302-0001). Primer sequences of other genes of interest are shown in Table P < 0.05. All bar graphs are presented as the mean ± SEM.Statistical analyses were performed using GraphPad Prism 8.0 . To compare statistical differences among each group, one-way ANOVA was used. The definition of statistical significance is when *P < 0.01) in DHCR24-overexpressing cells  and vector group  cells. And the DHCR24 protein level was significantly reduced (decreased to 12%) Fig. , suggestlls Fig. . A signi, Ser199, Thr231, Ser262, and Ser396 sites, as these sites have been tightly associated with tau pathology in AD [, Ser199, Thr231, Ser262, and Ser396 was observed, as compared to the control groups (BL and NC1). Of note, among these sites, the phosphorylation level of Ser396 reached its maximum (2.38-fold). In contrast, Thr181 was the least affected site after DHCR24 knockdown (only 1.59-fold). Moreover, quantitative analysis showed that the phosphorylation level of tau Ser199, Thr231, and Ser262 was increased by 2.15 ± 0.12, 1.85 ± 0.20, and 2.01 ± 0.14, respectively, in DHCR24 lower-expressing C8D1A cells. Thus, these findings suggest that DHCR24 downregulation could cause tau hyperphosphorylation in astrocytes. In addition, we further tested whether increasing DHCR24 levels would reverse the above effects. As shown in Fig. , Ser199, Thr231, Ser262, and Ser396 were all dramatically decreased, leading to a 55.72%, 53.01%, 35.72%, 69.26%, and Ser396 78.42% decrease in tau phosphorylation, respectively. The above outcomes suggest that DHCR24 overexpression could suppress tau hyperphosphorylation in C8D1A astrocytes. Furthermore, we observed no alteration in the protein level of total tau among all groups, as recognized by the Tau5 antibody. Altogether, our findings unveil for the first time that DHCR24 is involved in the regulation of astrocytic tau phosphorylation in C8D1A astrocytes.Tau tauopathy in astrocytes from patients with Alzheimer’s disease or other tauopathies were detected to be phosphorylated at sites of Thr181, Ser199, Ser202, Ser214, Thr231, Ser262, Ser396, and Ser422 , 11. To Given that DHCR24 is such a crucial molecule regulating cholesterol de novo synthesis, we next assess the change of cellular free cholesterol content by Filipin III staining. Filipin III is a florescent that can specifically label cellular free cholesterol, including cholesterol in the plasma membrane . As showAs twenty-some enzymes are involved in cholesterol synthesis, and previous studies have shown that feedback regulation of these enzymes initiates when cellular cholesterol content is altered , we therWe additionally assessed the mRNA level of acyl coenzyme A:cholesterol acyltransferase (ACAT), which plays an important role in maintaining cellular cholesterol homeostasis. Abundant cellular cholesterol serves as an ACAT activator, and then, this enzyme esterifies free cholesterol to cholesterol esters which can be stored in lipid droplets , 42. TheTo further explore the change of cholesterol esterification after DHCR24 knockdown, we performed Oil Red O staining to analyze the change in the intracellular cholesterol ester content. Oil Red O is a fat-soluble dye that specifically stains triglycerides and cholesteryl oleate but no other lipids . Hence, We at a previous experiment have observed that DHCR24 downregulation could lead to decreased cellular free cholesterol levels and reduced cholesterol esters, as shown by Filipin staining and Oil Red O staining. And the altered transcription level of enzymes involved in cholesterol metabolism has also suggested the disruption of cellular cholesterol homeostasis. To further verified that cholesterol homeostasis was disturbed after the change of DHCR24 expression, we next applied Amplex Red cholesterol quantification kit to assess total cholesterol content. As shown in Fig. Given that cholesterol is a major component of caveolae, and the content of plasma membrane cholesterol was affected when DHCR24 expression was altered Fig. , we postIt has been suggested that activation of MAPK/ERK promotes tau phosphorylation, and this signaling pathway was activated in AD pathology and in astrocytes bearing phosphorylated tau –49. Ther, Ser199, Ser262 and Ser396 was observed, as compared to the control culture. Among these sites, Ser396 was the most affected one because its phosphorylation level reached its minimum, suggesting that MEK/ERK signaling mainly regulates phosphorylation of Ser396 site in DHCR24 knockdown astrocytes. Of note, the phosphorylation of tau at these sites were not completely inhibited even at the highest concentration (100 µM) of U0126 when p-ERK can barely be detected, which implies that MEK/ERK signaling partially regulates tau phosphorylation in C8D1A astrocytes. In other words, multiple kinases or signaling pathway could together contributes to tau phosphorylation, which is in line with previous research [To determine whether activation of Ras/MEK/ERK signaling pathway is involved in tau hyperphosphorylation after DHCR24 knockdown in astrocytes, we applied a known MEK inhibitor, U0126, in the culture medium of DHCR24 lower-expressing cells. U0126 is capable of preventing the activation of MEK1 and MEK2, selectively, thus the subsequent inactivation of p-ERK . DHCR24 research , 52. In In the present study, we found that silencing DHCR24 could lead to tau hyperphosphorylation at Thr181, Ser199, Thr231, Ser262 and Ser396 sites, while over-expressing DHCR24 could reverse the effects of tau phosphorylation at these sites by DHCR24 knockdown in astrocyte C8-D1A Cells. Tau-bearing astrocytes have been observed in the aged population, AD mouse model and AD patients, and overexpression of tau in astrocytes caused exhibition of abnormally phosphorylated and pathological conformation of tau , 53, 54.In previous study, we found that DHCR24 knockdown could lead to hyperphosphorylation of tau in neuronal SHSY-5Y cells, which is tightly related to cell cholesterol deficiency , 31. HowCholesterol is the major component of the cellular membrane and has been indicated to affect the construction and function of caveolae –70. In oIn the present study, the overactivation of the Ras/MEK/ERK signaling pathway was observed in DHCR24 knockdown astrocytes, while DHCR24 knock-in reduced the activation of this MAPK/ERK signaling. Caveolae have long been regarded as a vital platform for signal transduction and various signaling molecules locate in this construction. Ras is implied to locate in caveolae and interact with caveolae-associated protein –76. The Interestingly, a growing body of evidence reveals that the expression of major genes involved in cholesterol synthesis in the brain was obviously downregulated, including DHCR24, in aged mice, diabetic mice, familial Alzheimer’s disease (FAD) mice, and AD patients, which suggest the decrease in de novo cholesterol synthesis and brain cholesterol loss –25. FurtIn conclusion, in astrocytic C8D1A cells, we for the first time established an in vitro astrocytic cell model of cellular cholesterol deficiency by DHCR24 knockdown. We found that DHCR24 knockdown induced the decrease in plasma membrane cholesterol and disruption of lipid raft/caveolae, resulting in activation of lipid raft-dependent Ras/MEK/ERK signaling pathway Fig. . Besides


Metarhizium acridum were analyzed using gene knockout and rescue strategies. Deletion of MaSln1 did not affect conidial germination, conidial yield, or resistance to chemical agents. However, fungal tolerance to heat shock and UV-B were significantly reduced after deletion of MaSln1. Insect bioassays showed that fungal pathogenicity was significantly impaired when MaSln1 was deleted. Further studies showed that MaSln1 did not affect either germination or appressorium formation of M. acridum on locust wings, but it significantly increased appressorium turgor pressure. In addition, disruption of MaSln1 resulted in a conidiation pattern shift in M. acridum. Microscopic observation revealed, however, that some genes located in the MAPK signaling pathway, including MaSho1, MaHog1, MaMk1, and MaSlt2, were not involved in the conidiation pattern shift on SYA medium (microcycle medium). Meanwhile, of the 143 differently expressed genes (DEGs) identified by RNA-seq, no genes related to the MAPK pathway were found, suggesting that MaSln1 regulation of the conidiation pattern shift was probably independent of the conserved MAPK signaling pathway. It was found that 22 of the 98 known DEGs regulated by MaSln1 were involved in mycelial growth, cell division, and cytoskeleton formation, indicating that MaSln1 likely regulates the expression of genes related to cell division and morphogenesis, thus regulating the conidiation pattern shift in M. acridum.As a conserved sensor kinase in the HOG-MAPK pathway, Sln1 plays distinct functions in different fungi. In this study, the roles of MaSln1 in IMPORTANCE The productivity and quality of conidia are both crucial for mycopesticides. In this study, we systematically analyzed the roles of MaSln1 in fungal pathogens. Most importantly, our results revealed that deletion of MaSln1 resulted in a conidiation pattern shift in M. acridum. However, some other genes, located in the MAPK signaling pathway, were not involved in the conidiation pattern shift. RNA-seq revealed no genes related to the MAPK pathway, suggesting that the regulation of the conidiation pattern shift by MaSln1 was probably independent of the conserved MAPK signaling pathway. This study provided a new insight into the functions of Sln1 and laid a foundation for exploring the mechanisms of conidiation pattern shifts in M. acridum. Metarhizium acridum, a locust-specific pathogen, and microcycle conidiation exhibited great potential for improving the productivity and quality of conidia  signaling pathway plays important roles in fungal growth and development, such as cell cycle, morphogenesis, stress resistance, virulence, cell wall rigidity and integrity, intercellular signal transduction, etc. . These cidiation . The Sltidiation , 16. In cterized , 18. In isopliae –22.Two-component systems (TCSs), which are widely present in bacteria, higher plants, and fungi, are involved in cell osmotic stress adaptation, cell cycle, growth and development, and secondary metabolites . The bas24–S. cerevisiae, Sln1 is indispensable for fungal growth  has no effect on mycelial development and morphology  marneffei, a PmSlnA (the homologue of Sln1)-deletion mutant exhibited delayed conidial germination and yield and increased susceptibility to high osmotic pressure and cell wall-disturbing chemicals  in the model fungal pathogen Metarhizium acridum through gene knockout and rescue strategies. Our results showed that the deletion of MaSln1 did not affect conidial germination, conidial yield, or resistance to most of the chemical agents tested. However, fungal sensitivity to UV-B and heat shock were significantly increased in ΔMaSln1. Bioassays showed that fungal pathogenicity was significantly reduced when MaSln1 was deleted. Interestingly, because MaSln1 is an upstream transmembrane protein, its regulation of the conidiation pattern shift in SYA medium may be independent of the conserved MAPK pathway in M. acridum. RNA-seq was used to reveal the mechanism by which MaSln1 regulates the conidiation pattern shift. The results indicated that MaSln1 may regulate the expression of genes related to cell division, mycelial growth, and cytoskeleton remolding, thus regulating the conidiation pattern shift in M. acridum.In this research, we clarified the roles of MaSln1 (MAC_05900) coding sequence is 3,426 bp with a 60-bp intron, encoding a protein with 1,121 amino acids. The isoelectric point and molecular weight are 6.88 and 122.8 kDa, respectively, as determined through bioinformatic analysis. The web resource SMART (http://smart.embl.de/) was used to predict the conserved domain of MaSln1. Three transmembrane domains (TM), a histidine kinase domain (HiskA), a phosphor-transferase domain (HATPase_C), and a receptor domain (REC) are included . Southern blotting revealed that MaSln1 was successfully deleted and rescued .The full-length included . The neiincluded  using MEMaSln1 on fungal growth, conidial germination rates and conidial yields were both counted. Compared with that of the wild-type (WT) and complemented mutant (CP) strains, conidia germinated slowly on 1/4 Sabouraud dextrose agar plus yeast (SDAY) medium when incubated for 4 or 6 h , but reached the same level after 8 h incubation. The half-germination times (GT50s) of the strains showed no statistical differences . Additionally, deletion of MaSln1 did not affect conidial yield .To access the effect of MaSln1-deletion mutants was significantly impaired (50s) by UV-B irradiation for the WT and CP strains were 5.87 ± 0.42 h and 6.56 ± 0.36 h, respectively, while the average IT50 of the MaSln1-deletion mutant was 4.19 ± 0.14 h (P < 0.05). Reverse transcription-quantitative PCR (qRT-PCR) results revealed that the expression levels of genes related to DNA damage repair were all significantly decreased in the MaSln1 mutant compared with those in the WT . The IT50 of ΔMaSln1 was 5.60 ± 0.17 h, significantly decreased compared to that of the WT (8.65 ± 0.42 h) and CP . Expressions of genes related to heat shock were also downregulated in ΔMaSln1 .To determine fungal sensitivity to UV-B irradiation and heat shock, conidial germination rates were calculated. UV-B tolerance in impaired . The haln the WT . Similar ΔMaSln1 . In addiMaSln1 was significantly increased compared to that of the WT and CP, (P < 0.05). The half-lethal times (LT50s) for the WT, ΔMaSln1, and CP were 7.84 ± 0.19, 8.62 ± 0.29, and 7.86 ± 0.26 days, respectively (P < 0.05). However, the LT50s of these three strains when they were administered via intrahemocoel injection showed no significant differences (P > 0.05). These results indicated that MaSln1 contributes to fungal virulence during adhesion and/or cuticle penetration.Bioassays were performed using two methods. Topical inoculation showed that the survival of ΔMaSln1 contributed nothing to appressorium formation and conidial germination on locust wings (P > 0.05). However, the rate of collapsed appressoria was significantly reduced in MaSln1 mutants treated with PEG-8000 .Further studies were conducted to explain the reason for the decreased virulence during topical inoculation. Unexpectedly, deletion of PEG-8000 , indicatMaSln1 deletion strain exhibited a different conidiation pattern on SYA medium. At 14 h, the WT and CP strains began to produce conidia with arrested hypha elongation -staining revealed that MaSln1 contributed to chitin distribution in hyphae. Compared with that in the WT and CP strains, the chitin in ΔMaSln1 was distributed not only in the septa and the tips of hyphae, but also irregularly in the extended hyphae , samples of WT and ΔMaSln1 cultured on SYA medium for 14 h were collected for analysis. A total of 143 DEGs were isolated to be further analyzed. Among the 143 DEGs, 61 (∼42.7%) were upregulated and 82 (∼57.3%) were downregulated , a surface protein (Mas1) (MAC_03649), a dual-specificity phosphatase Yvh1 (MAC_01700), and a pantothenate transporter (MAC_06827). In addition, genes related to cell cycle and regulation of cell differentiation were also dramatically up- or downregulated in ΔMaSln1. Three DEGs were involved in cell cycle, such as genes for an ATG5 protein (MAC_03903); a multidrug-resistance protein, Tpo1 (MAC_03963); and a putative oxidoreductase. Additionally, three genes involved in cytoskeleton remodeling were also remarkably downregulated. As a result, MaSln1 deletion probably shifts the conidiation pattern by regulating the expression levels of genes which are crucial for conidiation, cell growth, cell cycle, and cytoskeleton remodeling.To reveal the role of ervation  and MaSlegulated . Among t  in the MAPK pathway were not involved in the conidiation pattern shift on SYA medium, indicating that MaSln1 regulation of the conidiation pattern might not rely on the conserved MAPK pathway. Therefore, RNA-seq was used to screen the DEGs regulated by MaSln1 during a conidiation pattern shift.Sln1 is a transmembrane protein upstream from the HOG-MAPK signaling pathway. In this study, gene knockout and recovery methods were used to obtain deletion and complementation strains of MaSln1 does not affect conidial germination or conidial yield, consistent with observations in a FgSln1-deletion mutant of F. graminearum  was significantly downregulated in ΔMaSln1 compared with that in the WT, indicating that MaSln1 might contribute to thermotolerance in M. acridum by regulating the expression of genes related to heat shock response. In addition, genes involved in DNA repair  in ΔMaSln1 were also downregulated, indicating that MaSln1 may also affect these genes to regulate fungal UV-B resistance. In M. oryzae, deletion of Sln1 reduced fungal sensitivity to CFW and increased sensitivity to oxidative and osmotic pressure  located in the MAPK signaling pathway contributed nothing to the conidiation pattern shift. What is the possible mechanism by which MaSln1 affects conidiation pattern shifts in M. acridum? RNA-seq was conducted to screen certain DEGs. RNA-seq data indicated that some of these DEGs were involved in fungal growth, conidiation, cell cycle, and cell division. Therefore, we speculate that MaSln1 affects the expression of these genes to shift the conidiation pattern.It is worth mentioning that MaSln1 is involved in the conidiation pattern shift, probably independent of the conserved MAPK signaling pathway in Sln1 was involved in mycelial morphology development  which plays important roles in conidia formation and cell cycle in M. anisopliae  which can act as a molecular bridge by inhibiting the nucleation activity of actin, thus promoting cell membrane flow , which is important for fungal morphogenesis, was upregulated  (MaHog1-disruption (ΔMaHog1) (MaMk1-disruption (ΔMaMk1) mutants  was used. Agrobacterium tumefaciens AGL-1 was used for fungal transformation . MaSln1 was deleted using the split-marker strategy. To construct the MaSln1-deletion vectors, the primer pairs Sln1-LF/Sln1-LR and Sln1-RF/Sln1-RR were used to amplify the sequences up- and downstream from MaSln1. The resulting fragments were inserted into pK2-SM-F and pK2-SM-R to yield the MaSln1-deletion vectors pK2-SM-MaSln1-F and pK2-SM-MaSln1-R, which were transformed into the WT to screen the MaSln1-deletion strains. For MaSln1 complementation, the 5′-flanking region and MaSln1 open reading frame (∼5.6 kb) were cloned into the plasmid pK2-Sur-GFP, which harbored a Sur cassette, to yield pK2-MaSln1-Sur-GFP vector. The resultant vector was integrated into ΔMaSln1. The complemented mutants were screened on Czapek-Dox medium containing 20 μg/mL chlorimuron ethyl . Fungal transformation mediated by A. tumefaciens and transformant validation were performed as described previously . All the primers used in this study are listed in Table S1 in the supplemental material.The wild-type strain normally . The MaSΔMaSho1) , MaHog1-ΔMaHog1) , and MaM mutants  have beeeviously . For MaS7 cells/mL, 100 μL) of microbial strains in Tween 80 (0.05%) were inoculated evenly on 1/4 SDAY  plates, then incubated at 28°C. Germination percentage was examined every 2 h. For conidial yield, 2 μL of suspensions (1 × 107 cells/mL) were inoculated into 24-well plates containing 1/4 SDAY (1 mL). After incubation at 28°C for 3, 6, 9, 12, and 15 days, conidia in each well were collected and suspended with 0.05% Tween 80, respectively, and examined with a hemocytometer. To access fungal sensitivities to cell wall-disturbing agents, conidia suspensions  were inoculated on 1/4 SDAY or 1/4 SDAY with Congo Red  and Calcofluor white (30 μg/mL). Colonies were recorded by taking photos after 5 days cultivation. Fungal heat shock and UV-B tolerance assays were both conducted as described previously (7 conidia/mL) in sterile centrifuge tubes were treated in a water bath at 45°C for 3.0, 6.0, 9.0, and 12.0 h, respectively, and 100 μL of treated suspensions were spread evenly on SDAY plates, then cultured in darkness at 28°C for 20 h. For UV-B tolerance assays, 100 μL of conidial suspensions (1 × 107 conidia/mL) were spread evenly on SDAY plates. The plates were immediately exposed to UV-B radiance at a dose of 1,350 mW/m2 for 1.5, 3.0, 4.5, and 6.0 h, respectively. The plates were immediately incubated in darkness at 28°C for 20 h. Conidial germinations were detected through microscopic observation. Experiments were based on biological triplicates.For the conidial germination assay, conidial suspensions  were inoculated on locusts. Alternatively, locusts were injected via the hemocoel with 5-μL suspensions prepared in double-distilled water . Negative controls for both methods were inoculated with 5 μL of pure paraffin oil or 5 μL sterile ddH2O. Survival of locusts was calculated every 12 h. Each group included 30 insects and was based on three replicates.Two infection modes, cuticle infection and intrahemocoel injection, were used to detect fungal virulence toward the fifth-instar locusts. For cuticle infection, conidial suspensions in paraffin oil  of WT, ΔMaSln1, and CP strains were spread evenly onto SYA  medium. Fungal growth was recorded every 2 h by microscopic observation. CFW staining (1 μg/mL) was used to detect fungal cell wall chitin distribution  was performed with phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) based on the hypergeometric test. Bonferroni was used for significance level correction  with a rigorous threshold. qRT-PCR was used to validate the reliability of the RNA-seq data, which are presented in Table S2.Conidial suspensions  test was used to determine statistical differences among the fungal strains.PRJNA739483.The RNA-seq data in this study have been deposited in the NCBI BioProject database under the accession number