Drosophila Kenyon cells of the mushroom body (MB), a circuit with structural characteristics reminiscent of vertebrate cortical networks. This model recapitulates several aspects of dementia-relevant pathological features including age-dependent neuronal loss, nuclear depletion and cytoplasmic accumulation of TDP-43, and behavioral deficits in working memory and sleep that occur prior to axonal degeneration. RNA immunoprecipitations identify several candidate mRNA targets of TDP-43 in MBs, some of which are unique to the MB circuit and others that are shared with motor neurons. Among the latter is the glypican Dally-like-protein (Dlp), which exhibits significant TDP-43 associated reduction in expression during aging. Using genetic interactions we show that overexpression of Dlp in MBs mitigates TDP-43 dependent working memory deficits, conistent with Dlp acting as a mediator of TDP-43 toxicity. Substantiating our findings in the fly model, we find that the expression of GPC6 mRNA, a human ortholog of dlp, is specifically altered in neurons exhibiting the molecular signature of TDP-43 pathology in FTD patient brains. These findings suggest that circuit-specific Drosophila models provide a platform for uncovering shared or disease-specific molecular mechanisms and vulnerabilities across the spectrum of TDP-43 proteinopathies.Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) comprise a spectrum of neurodegenerative diseases linked to TDP-43 proteinopathy, which at the cellular level, is characterized by loss of nuclear TDP-43 and accumulation of cytoplasmic TDP-43 inclusions that ultimately cause RNA processing defects including dysregulation of splicing, mRNA transport and translation. Complementing our previous work in motor neurons, here we report a novel model of TDP-43 proteinopathy based on overexpression of TDP-43 in a subset of The online version contains supplementary material available at 10.1186/s40478-023-01656-0. TARDBP, the gene encoding TDP-43, have been identified as causative of ALS [Characterized by extensive overlap in cellular pathology, genetic mutations, and molecular markers, amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) comprise a neurodegenerative disease continuum affecting multiple subsets of neurons in the spinal cord, motor cortex, cingulate cortex, and frontal and temporal lobes –4. Notabe of ALS –9 while e of ALS , 11. Alte of ALS .Substantiating its significance as a marker of pathology, TDP-43 has also been identified as a component of cytoplasmic aggregates in dementias other than FTD, including Alzheimer’s disease (AD) –15, AD wWild-type TDP-43 is localized primarily to the nucleus where it regulates RNA transcription  and spliDrosophila melanogaster) genome contains a homolog of TARDBP, namely TBPH, which displays sequence similarity and functional overlap with human TDP-43 [Following the identification of TDP-43 as a protein of pathological interest in numerous neurodegenerative diseases, model organisms have been critical to understanding the normal biological functions of TDP-43 and how disruption of these functions leads to disease. Despite having diverged from a common ancestor with vertebrates over 600 million years ago , the frun TDP-43 , 43.Drosophila have recapitulated the locomotor phenotypes, motor neuron dysfunction, and shortened lifespan [In addition to extensive overlap in neurogenetic homology, circuit-level conservation in the central nervous system has also been demonstrated between invertebrates and vertebrates –46, makilifespan , 54, 55 lifespan , 38, 56 lifespan –60, and lifespan , 62.While TDP-43 proteinopathy has been extensively studied in motor neurons and glia as a model of ALS, dementia modeling based on TDP-43 proteinopathy in flies has been limited. This is in part because the mushroom bodies (MBs) of the fly brain, which were originally described as essential to odor learning  have onldlp mRNA, encoding the heparan sulfate proteoglycan (HSPG) Dally-like protein (Dlp)/GPC6, which we previously found to be a target of TDP-43 in fly models of ALS and is altered in human ALS spinal cords [dlp mRNA is enriched in TDP-43 complexes in MBs while Dlp protein is decreased within axons during aging, consistent with TDP-43 dependent axonal transport and/or translation deficits in an age-dependent manner. Notably, Dlp overexpression in MBs rescues TDP-43 dependent deficits in working memory consistent with Dlp being a physiologically significant target of TDP-43 in the MB circuit. These findings demonstrate that TDP-43 proteinopathy in MBs causes dementia-like phenotypes that are mediated at least in part by Dlp/GPC6, a regulator of the Wg/Wnt signaling pathway. Further substantiating the link between Dlp/GPC6 and TDP-43 pathology, we found GPC6 mRNA to be altered in FTD patient brains. Specifically, neuronal nuclei expressing STMN2 and KALRN cryptic exons associated with TDP-43 nuclear depletion show an upregulation of GPC6 mRNA when compared with nuclei that retain TDP-43 as evidenced by the presence of canonically spliced STMN2 and KALRN junctions. Lastly, we report a number of candidate targets of TDP-43 that show overlap between our fly models and patient brains, suggesting these models will be useful for studying molecular pathyways underlying distinct neuronal vulnerabilities across the spectrum of TDP-43 proteinopathies.Here we describe the development of a novel fly model of dementia based on TDP-43 proteinopathy induced by specific over-expression of wild-type or mutant human TDP-43 in a well-defined subset of MB neurons. Our model recapitulates key cellular and behavioral characteristics of human dementias with TDP-43 pathology, including age-dependent loss of nuclear TDP-43, axonal degeneration, and working memory and sleep deficits that parallel those observed in dementia patients. Using RNA immunoprecipitations we show that in MBs, TDP-43 associates with several mRNAs, a subset of which are unique to MBs while others are shared with mRNA targets previously identified in motor neurons . Interesal cords . Here weDrosophila lines used in this study and specific experiments where each line was employed can be found in the Key Resources table  w1118; TDP-43WT::YFP, (2) w1118; TDP-43WT::YFP; dlp OE, (3) w1118; TDP-43WT::YFP mCD8::RFP, and (4) w1118 in triplicate. Heads (N = 15 for each genotype) were decapitated and homogenized in 100 µl 2X Laemmli Sample Buffer (BIO-RAD 1610737) containing 5% of 2-Mercaptoethanol (Sigma-Aldrich M3148). The homogenized protein samples were boiled for 5 min in the digital Heat Block (Benchmark), and spun for 1 min. Supernatants were collected and 10 µl of protein sample was loaded in each well of precast Mini-PROTEAN TGX 4–20% gradient Gel (BIO-RAD 4561096). Following SDS-PAGE, the proteins were transferred to Nitrocellulose membrane (BIO-RAD 1620215). After transfer, the membrane was blocked in 5% nonfat milk in PBST (PBS and 0.1% TWEEN20) and incubated overnight at 4 °C with primary antibodies mouse anti-GFP Living Color  to detect TDP-43 YFP and rabbit anti-Beta-Actin  as loading control, followed by washes in TBST and incubation with secondary antibody  and  for 1 h at RT. The blot was imaged using a LICOR scanner (Odyssey®DLx) and protein bands intensities were quantified with LICOR “Image Studio Lite” , they were pooled to increase power. Where necessary, outliers were removed prior to hypothesis testing. When data did not fit the assumptions of parametric models and non-parametric analyses were used, hypothesis testing proceeded by first using a Kruskal-Wallace to test for a difference among groups, followed by pairwise comparisons using the Wilcoxon Rank Sum Test with p-values adjusted for multiple comparisons using the false discovery rate method [Statistical analyses were conducted in R [v. 4.1.2, R Core e method . SpecifiSS01276 [Mushroom body morphology was evaluated using membrane-targeted RFP (mCD8 RFP) driven by SS01276 . Adult bSS01276 , fixed flm function in R; R Core Team 2017). Residuals were normally distributed  and we therefore continued with pairwise comparisons using Tukey’s Honest Significant Difference corrected for multiple comparisons.TDP-43 cytoplasmic localization and MBN cell loss: Images were acquired using a Zeiss 880 Laser Scanning Confocal inverted microscope with a Plan-Apochromat 63x/1.4 oil DIC M27 lens. TDP-43 YFP signal intensity was quantified per unit area in the nucleus and cell body. Hoechst was used to define the nuclear boundaries, while total cellular TDP-43 signal was measured by tracing a boundary around YFP signal in the entire cell . This polygon was then dragged to an adjacent area of the brain to obtain a mean fluorescence intensity for the background in each section. In instances where the polygon was shaped in such a way that it could not be dragged to adjacent brain area, a comparably-sized polygon was drawn in the adjacent brain region. Intensity was measured in at least three sections to obtain mean fluorescence intensity in each channel (RFP or FITC) for both α/β and γ lobes of each brain. To test for differences in signal intensity within a genotype across age time points, values of signal intensity were normalized to mean signal intensity of young (1–3 days old) brains of the same genotype and lobe. Statistical analysis of change in fluorescence intensity with age was performed by comparing middle aged (~ 30 days) or old (~ 60 days) flies and young (~ 1 day) flies from the same genotype using the Wilcoxon Rank Sum test. Summary statistics can be found in Additional file 2 in size and showed 0.25–1.0 circularity. To measure mean particle size in each sample, a polygon was drawn over the visible portion of each lobe in at least three sections for each lobe. From these subsamples we calculated a mean particle size for each lobe of each brain. Summary statistics can be found in Additional file YFP particle size: The Analyze Particles function in FIJI was used to identify YFP puncta in MB lobes. Images were first contrast enhanced using a brightness/contrast enhancement  followed by thresholding using the Bernsen method in the Auto Local Threshold function with a 25–50 pixel radius depending on image quality. Particles were considered puncta when they were 0.25–3.0 µmDally-like protein expression during aging in MBs: Brains from flies 1–3 days or 50–55 days were dissected out and the tissue fixed, permeabilized and blocked as described above. Brains were then incubated overnight at 4 °C in an anti-Dally-like protein antibody at 1:5 in block , rinsed 3X in 0.1% PBST and incubated in goat anti-mouse Alexa Flour 647 antibody overnight at 4 °C . Images were acquired using a Zeiss 880 Laser Scanning Confocal inverted microscope with a Plan-Apochromat 63×/1.4 oil DIC M27 lens. Signal intensity was traced from maximum intensity projections of the MBLs and normalized to background intensity. Dlp signal intensity declined with age in TDP-43 flies, so the mCD8 RFP channel was used to trace the lobes in aged brains, ensuring the correct lobe area was used for signal intensity measurements.WT expression, in addition to western blots, we also measured mean pixel intensity from maximum intensity projections of the horizontal lobes (β and γ combined in maximum intensity projection) in two genotypes with SS01276-driven transgenes, OR-R;TDP-43WT::YFP and w1118; TDP-43WT::YFP mCD8::RFP . TDP-43WT also showed a bias for arm A (P = 0.0033), while OR-R controls showed a bias for arm C (P = 0.048). Although these analyses suggest that arm biases exist in males and are not unique to TDP-43 overexpression, a single replicate was used to test whether Dlp over-expression could rescue alternation deficits seen in TDP-43 overexpressing flies therefore hypothesis testing proceeded with non-parametric tests as described in Statistical analyses.Y-maze: To measure changes in working memory, we employed a y-maze assay described by Lewis, Negelspach, Kaladchibachi, Cowen and Fernandez . We tracDrosophila Activity Monitors [DAMs; Trikinetics, Waltham, MA; Drosophila, sleep is defined at five minutes of inactivity [Sleep: Adult flies from the three age time points were monitored individually using activity  and heresurvival [survivminer [Newly eclosed virgin males and females were separated and placed on fresh food, then transferred weekly into new food vials. The number of surviving flies was counted every other day for 100 days. Survival analysis and plots were generated using R packages survival  and survvivminer .WT YFP, or TDP-43G298S YFP were collected for each of three biological replicates. Frozen flies were then transferred to 50 mL conical tubes and heads were separated from the bodies using four rounds of vortexing and flash freezing with liquid nitrogen. A sieve was used to filter out the bodies and isolated heads were collected into tubes containing lysis beads (Next Advance Green lysis beads). Heads were homogenized (Next Advance Bullet Blender) in one mL fresh lysis buffer , protease inhibitors , and RNAsin Plus 400 u/mL (Fischer Scientific PRN2615), centrifuged for 10 min at 10,000 rpm, and the lysate was collected. A portion of the lysate was saved for the protein input and RNA input samples. For protein input, the lysate was mixed with 2X Laemmli buffer, boiled at 95–100 °C for 10 min, and stored at − 20 °C. For RNA input, TRIzol reagent (Thermofisher 15596026) was added to the lysate and the sample was stored at − 80 °C. High affinity GFP-Trap magnetic agarose beads (ChromoTek) were added to the remaining lysate and rotated end-over-end for 90 min at 4 °C to allow for binding of YFP. The beads were separated from the lysate with a magnet and washed three times with fresh wash buffer . The beads were resuspended in wash buffer and split into two tubes for the protein IP and RNA IP samples. For the protein IP sample, 2X Laemmli buffer was added to the beads, samples were boiled at 95–100 °C for 10 min, and the beads were removed with a magnet. Western blots (see above) were performed to ensure that the immunoprecipitated complexes contained TDP-43 before processing the RNA IPs. For the RNA IP sample, TRIzol reagent was added to the beads, the solution was pipetted up and down for 60 s, and the beads were removed with a magnet.RNA Immunoprecipitations: Equal numbers of male and female flies, aged one to three days, were pooled in Eppendorf tubes and flash frozen in liquid nitrogen. A minimum of 500 flies expressing YFP, TDP-43futsch was recovered as enriched with TDP-43 in both variants  following manufacturer’s protocol. Determination of cDNA quality and quantity was determined via Agilent Tapestation High Sensitivity D5000 Screentape and Qubit dsDNA HS assay for input into library amplification. Libraries were quantified by Agilent Tapestation High Sensitivity D1000 Screentape and Kapa Library Quantification kit for Illumina platforms. Libraries were pooled equimolarly, pools were quantified by Agilent Tapestation High Sensitivity D1000 Screentape and Kapa Library Quantification kit and loaded on the NovaSeq 6000 S4 flowcell and sequenced to 101 × 11 × 11 × 101 cycles. Trimmed fastqs were aligned to the Dmel genome with STAR v2.6.1d . AlignedFunctional annotation: The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to functionally annotated genes enriched with TDP-43 , 93. To RNA Extraction for Validation: RNA input and RNA IP samples, stored in TRIzol reagent (Thermofisher 15596026) at − 80 °C, were allowed to thaw completely on ice and chloroform was added to each sample. Samples were shaken briefly to mix, left to incubate for three minutes at room temperature, and then centrifuged at 12,000 rpm for 15 minutes at 4 °C to allow for separation of the aqueous and organic phases. The RNA-containing aqueous phase was collected and molecular grade isopropyl alcohol was added to each sample. Samples were incubated for 10 min at room temperature for RNA precipitation and then centrifuged at 12,000 rpm for 10 minutes. The pelleted RNA was washed with 75% ethanol (200 proof ethanol & HyPure water) and left to dry in a fume hood. RNA was resuspended in HyPure water and the concentration and 260/280 absorbance ratio were measured using Nanodrop.RT-qPCR: RNA extracted from input and IP samples was used as template RNA for cDNA synthesis by reverse transcription. Total RNA used in the cDNA synthesis reactions was normalized across samples. cDNA was synthesized using the Fisher First Strand cDNA synthesis kit (Thermofisher Scientific K1641). qPCR reactions were prepared in a 96-well qPCR plate, in triplicates, using Taqman Fast Advanced Master Mix (Thermofisher Scientific 4444556) and Taqman probes for Dally-like protein (dlp) (Thermofisher Scientific Dm01798597_m1) and Gpdh1 (Thermofisher Scientific Dm01841185_m1). qPCR was conducted on the qTOWER  qPCR machine. Delta Ct was calculated as the difference in Ct values (dlp—gpdh1). To determine enrichment of dlp mRNA in the IPs, ΔΔCt was calculated as the difference in Delta Ct values (IP—Input). Fold change was calculated as 2^(− ΔΔCt).Dally-like protein target validation: Brains from flies one to three days or 50–55 days were dissected out and the tissue fixed, permeabilized and blocked as described above. Brains were then incubated overnight at 4 °C in an anti-Dally-like protein antibody at 1:5 in block , rinsed 3X in 0.1% PBST and incubated in goat anti-mouse Alexa Flour 647 antibody overnight at 4 °C . Images were acquired using a Zeiss 880 Laser Scanning Confocal inverted microscope with a Plan-Apochromat 63×/1.4 oil DIC M27 lens. Signal intensity was traced from maximum intensity projections of the MBLs and normalized to background intensity. Dlp signal intensity declined with age in TDP-43 flies, so the mCD8 RFP channel was used to trace the lobes in aged brains, ensuring the correct lobe area was used for signal intensity measurements.STMN2 or KALRN cryptic exon or a STMN2 or KALRN canonical junction, as defined in [GPC6 gene expression was compared between neuronal nuclei containing STMN2 and KALRN cryptic exons and neuronal nuclei containing STMN2 and KALRN canonical splice junctions using the independent t-test function (ttest_ind) in python. Violin plots were generated between neuronal nuclei containing a cryptic exon and neuronal nuclei containing a canonical exon using the scanpy violin function.snRNA seq analyses from FTD patient brains: Log normalized gene counts tables from  were filfined in . Human ofined in . GPC6 geSS01276 available from the Janelia FlyLight Split-GAL4 Driver Collection [Drosophila MBs. Previous studies have shown that neurons forming the γ and α′/β′ lobes are embryonic and larval in origin, respectively [The insect MBs are comprised of large, complex neurons called Kenyon cells, referred herein as MB neurons (MBNs), which form a layered network with reticular feedback , structullection  to exprellection  in a subectively , and perectively .SS01276 in the 3rd instar larva (L3) and found that cytoplasmic YFP, YFP tagged TDP-43 (TDP-43::YFP) and membrane targeted RFP (mCD8:: RFP) are limited to MBNs  and TDP-43G298S OE brains . In L3, the MBs comprise approximately 60 γ lobe MBNs and 40 α′/β′ lobe MBNs [SS01276 drives expression in some combination of γ lobe or α′/β′ lobe MBNs. However, since adult expression of SS01276 was determined to be limited to γ and α/β lobe MBNs [SS01276 corresponds to γ lobe MBNs only.We characterized expression driven by obe MBNs  thus at obe MBNs , and givVisual inspection of larval MBNs indicated that TDP-43 YFP was largely confined to the nucleus in L3 brains Fig. a but becTDP-43WT::YFP mCD8::RFP 0.978 P < 0.0001; Additional file TDP-43G298S:: YFP mCD8::RFP 0.967 P = 0.0004; Additional file WT OE model  and females  to measure total daytime and nighttime sleep. Our experiments show that on average, both male and female TDP-43 OE flies appear to be lethargic; they spent a greater proportion of their time sleeping during both the day and night compared to controls (approximately 11% more for TDP-43WT OE and approximately 8% more for TDP-43G298S OE). However, there are subtle differences in how TDP-43 OE affects sleep patterns based on age and sex. While males consistently sleep more during the day, this effect is weaker in females where we found trends towards increased daytime sleep at some but not all age time points in each genotype  and female declines appear less steep  wh, 95% CI ; P < 0.0WT or TDP-43G298S from Drosophila heads overexpressing TDP-43 YFP in MBNs using the SS01276 split GAL4 driver. Next, we used DESeq2 [WT YFP and TDP-43G298S YFP complexes, respectively  , for a total of 1572 mRNAs enriched across both models . Notably, differential expression analyses also recovered previously identified TDP-43 target mRNAs including futsch  [dscam2  [To understand how TDP-43 overexpression influences the MBN proteome, we immunoprecipitated YFP-tagged TDP-43d DESeq2  to asses.57E−10)  and dsca2.42E−3) .Fig. 6mRWT OE and 42% of TDP-43G298S OE highly associated mRNAs in MBNs overlapped with those we identified previously in motor neurons (MNs) when modeling ALS by overexpressing TDP-43 with the D42 GAL4 driver  and frizzled 2 (fz2) [dally-like protein (dlp), a heparan sulfate proteoglycan (HSPG) that interacts with multiple Wnt ligands [dlp mRNA was also found to be enriched with TDP-43 in fly MNs, where TDP-43 proteinopathy causes depletion of Dlp from the neuromuscular junction and simultaneous accumulation of Dlp puncta in cell bodies [dlp mRNA is a target of TDP-43 in MBNs as predicted by RNA seq, we used RNA IPs followed by RT-qPCR and confirmed that dlp mRNA is enriched in immunoprecipitated TDP-43WT and TDP-43G298S complexes compared to input   receptor ligands  were ovel bodies . To valiWT OE we found 461 enriched mRNAs that are shared in both MB and MN, and 594 mRNAs uniquely enriched in MBs  in the context of TDP-43 proteinopathy could mitigate TDP-43 dependent working memory deficits. Using the Y-maze working memory assay we found that the percent alternation deficits displayed by TDP-43WT OE flies could indeed be mitigated by dlp OE in MBNs , the glypican GPC4 , heat shock 70 kDa protein 2, HSPA2 , and the frizzled family receptor protein FZD7 .To validate our findings from ata sets  and founDrosophila with transcripts altered in single neuronal nuclei containing TDP-43 associated cryptic exons  versus neurons in which canonical splice junctions were present (N = 377), we found several human orthologs to be altered including dlp’s ortholog, GPC6 mRNA, which was found to be significantly upregulated  study of FTD patients with TDP-43 pathology and harboring mutations in C9orf72, the most common form of inherited ALS/FTD . When coNeurodegenerative diseases have historically been classified by shared symptomology and post-mortem pathology. More recently, the integration of molecular markers and genetic testing has uncovered evidence that cell-type susceptibility and dysregulation of specific molecular pathways can interact to produce similar clinical presentations across different disease etiologies . These fA common pathological feature of several neurodegenerative diseases including ALS, FTD, and other dementias is the nuclear depletion of the RNA binding protein TDP-43 accompanied by the accumulation of cytoplasmic, ubiquitinated foci , 18, 134Drosophila melanogaster, we leveraged a split GAL4 driver line with limited expression to a subset of Kenyon cells . Notably, this model exhibits nuclear depletion and cytoplasmic accumulation of TDP-43, accompanied by age-dependent axonal degeneration and cell loss, all of which recapitulate key aspects of disease pathology. Importantly, the onset of FTD relevant behavioral symptoms is detectable prior to widespread degeneration, as observed in human disease. Additionally, this model identifies both novel, MB specific, and motor neuron-shared mRNA candidate targets that have previously been associated with TDP-43 pathology. Of the latter, here we chose to focus on the glypican Dlp, a Wg/Wnt signaling regulator. We show that Dlp is a functional target of TDP-43 in the MB circuit that mediates, in part, TDP-43 dependent working memory deficits in the fly model.It has been previously shown that overexpression of TDP-43 in all MBNs using the driver line OK107 GAL4 (RRID:BDSC_854) caused age-dependent cell loss and axonal degeneration in MBs, however degeneration severity varied widely across nearly isogenic individuals , 139 andNeurodegeneration in ALS/FTD is thought to be driven by both loss of nuclear function, as some nuclei become devoid of TDP-43, and cytoplasmic gain of toxic function, as evidenced by TDP-43 accumulation in cytoplasmic puncta. While thus far invertebrate models of TDP-43 proteinopathy have not faithfully recapitulated TDP-43 mislocalization e.g., , 142, heAxonal degeneration is evident only in older adult flies and differentially affects MBNs that form the α/β and γ lobes, with the latter showing the greatest degeneration severity. This is particularly interesting because MBNs that form the γ lobe are embryonic in origin and therefore older than the pupal-born MBNs that form the α/β lobes , 145, hiDrosophila model.Frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP) is pathologically classified based on the cortical layers affected, the number of dystrophic neurite, and the extent of neuronal cytoplasmic inclusions . This paIn humans, behavioral symptoms of FTD precede widespread degeneration by years suggesting that behavioral deficits arise prior to neuronal pathology, in the absence of complete loss of nuclear TDP-43 function or cytoplasmic accumulation . TherefoBehavioral variant frontotemporal dementia (bvFTD) is diagnosed based on often subtle behavioral symptoms including a lack of empathy, increased apathy, dysinhibition and deficits in executive function . FTD wasIn addition to observed behavioral deficits, flies with TDP-43 proteinopathy show an approximately 10% reduction in median lifespan. This may be roughly comparable to human patients where FTD exerts a subtle effect on lifespan. In humans, mean age at diagnosis is 61.9 for early onset disease (< 65 years) with mean survival after diagnosis around 8 years . AlthougDrosophila TDP-43  based on UG-richness [Sequestration of mRNAs is one the mechanisms by which TDP-43 is thought to contribute to neurodegeneration and identifying candidate mRNA targets has helped better understand disease etiology , 157. Alrichness . In futuFunctional analyses of the mRNAs associated with TDP-43 identify numerous cellular pathways including Wg and Hippo that have been previously implicated in sleep regulation in flies . Additiodlp, an mRNA target of TDP-43 that we previously identified in the ALS model of TDP-43 proteinopathy, and a known regulator of Wg signaling. Indeed, Wg signaling is dysregulated in ALS [dlp OE in MBNs mitigates TDP-43 dependent working memory deficits, as evidenced by improved alternation in the Y maze assay. Taken together, these results support the notion that Wg/Wnt signaling is altered in TDP-43 associated neurodegeneration and modulating its activity via Dlp mitigates behavioral deficits caused by TDP-43 proteinopathy. Further substantiating these findings, we found that the expression of GPC6 mRNA, a human ortholog of dlp, is altered in FTD patient brains [i.e., cryptic exon inclusion in specific TDP-43 transcriptional targets), consistent with a link between Dlp/GPC6 and TDP-43 pathology. We speculate that this increase in GPC6 mRNA in neurons with TDP-43 nuclear depletion may reflect a compensatory upregulation caused by a cytoplasmic decrease in protein levels that we detected in Drosophila MBN axons. Indeed, the same study by Gittings, Alsop et al. [Drosophila models to identify functionally relevant disease targets. Interestingly, recent GWAS studies identified GPC6 as a risk factor for AD in African Americans [dlp/GPC6 mitigate specific phenotypic aspects of FTD and identify additional targets across the spectrum of TDP-43 proteinopathies.For functional validation we chose to focus on d in ALS , 162, mRd in ALS  and WNT1d in ALS . Despitet brains , specifip et al. , identifp et al. . Althougmericans , 117. InAdditional file 1:Figure 1-supplement 1. Maximum intensity projections showing split Gal4 driver line SS01276 expression in the 3rd instar larva. Figure 1-supplement 2. TDP-43G298S localization in MBN cell bodies. Figure 1-supplement 3. Method for measuring the ratio of nucleus to total cell TDP-43 YFP from mean pixel intensity of MBNs. Figure 2-supplement 1. Mushroom body lobes (MBLs) show age-related, region-specific TDP-43G298S cytoplasmic accumulation and axonal fragmentation. Figure 4-supplement 1. TDP-43G298S overexpression in MBNs reduces arousal, increasing day and night sleep. Figure 5-supplement 1. Mutant TDP-43 overexpression in MBNs is sufficient to reduce lifespan. Figure 6-supplement 1. mRNAs enriched with TDP-43G298S overexpression in Drosophila MBs. Figure 6-supplement 2. Functional annotation of enriched targets in fly models of TDP-43 driven dementia. Figure 7-supplement 1. Dally-like protein is a target of TDP-43G298S in MBNs. Figure 7-supplement 2. TDP-43WT protein expression is not reduced by the presence of a second UAS-driven transgene. Figure 7-supplement 3. TDP-43WT YFP expression levels is not reduced by concomitant expression of a second UAS-driven transgene (mCD8 RFP).Additional file 2:Table S1E Nucleus to whole cell TDP-43 YFP ratios summarized in Figure 1e. Table S1F Summary statistics for cell number data presented in Figure 1f. Table S2A Summary statistics for mCD8RFP intensity presented in Figure 2a. Table S2B Summary statistics for YFP particle size values presented in Figure 2b. Table S3 Summary statistics for y-maze data presented in Figure 3. Table S4A Summary statistics for night sleep presented in Figure 4a. Table S4B Summary statistics for sleep bout length presented in Figure 4b. Table S4C Summary statistics for sleep bout number presented in Figure 4c. Table S6A Wild-type TDP-43 GOterms from differential gene expression presented in Figure 6. Table S6B Mutant TDP-43 GOterms from differential gene expression presented in Figure 6. Table S7CD Y-maze summary statistics for data presented in Figures 7c and 7d. Table S8 mRNAs enriched in nuclei with TDP-43 associated cryptic exons. Table S9 Summary of phenotypes observed in wild-type and mutant fly models.


Recently, the coding metasurface has gained significant attention due to its exceptional potential in controlling electromagnetic (EM) waves with the rapid development of wireless communication systems. Meanwhile, graphene shows tremendous promise for the implementation of reconfigurable antennas due to its high tunable conductivity and its unique property that makes it a very suitable material for realizing steerable coded states. In this paper, we first propose a simple structured beam reconfigurable millimeter wave (MMW) antenna using a novel graphene-based coding metasurface (GBCM). Different from the previous method, its coding state can be manipulated by altering the sheet impedance of graphene instead of bias voltage. Then, we design and simulate several most popular coding sequences, including dual-, quad-and single-beam-generated implement, 30° beam deflection, as well as a random coding sequence for radar cross-section (RCS) reduction. The theoretical and simulation results show that graphene has great potential for MMW manipulation applications, which lay a foundation for the subsequent development and fabrication of GBCM. The capability of manipulating electromagnetic (EM) waves, including the frequency , phase , magnituThe methodology of the coding beam reconfiguration needs a stable amplitude response and periodic gradient phase shifts in the range of 0–2π ,14,15,16In recent years, graphene has provided more abundant manipulation means for the regulation of EM waves by taking advantage of its tunable conductivity ,20. MeanIn this paper, for the first time, we proposed a new approach for the EM wave beam regulation with complex functions by combining the tunable sheet impedance of graphene with the coding MS, as is shown in The design of any MS starts with its basic unit cells. For beam manipulation, a unit cell needs to be able to simultaneously satisfy the ability to control the phase response and generate high reflection amplitude in a wide band ,20. TherNanomaterial graphene is a monolayer of carbon atoms arranged in a hexagonal lattice that can be modeled as an extremely thin conductive layer. The conductivity of graphene can be calculated using the Kubo formula ,30,31,32j is the imaginary unit, e = −1.6 × 10−19 C is the electron charge, −34 Js is the reduced Plank’s constant, −23 m2kg/s2K is Boltzmann’s constant, T = 300 K is the room temperature and τ is the relaxation time, At the THz band, graphene-based devices are enabled solely by the intraband transition. The graphene conductivity can be approximately expressed as:At the microwave band, based on the Kubo formula in Equation (1), the resistance of graphene impedance remains almost constant, while the reactance term tends to 0 and could be neglected. Therefore, graphene can be approximately treated as a resistive sheet without dispersion  and modeεr = 3.5) substrate, Taconic TLY-5 substrate (εr = 2.2), and the copper layer as GND. h1 = 0.075 mm height and placed on a Taconic TLY-5 substrate with h2 = 0.787 mm height. The spacing between the units should be a subwavelength to provide efficient scattering and prevent the occurrence of grating diffraction. However, it should not be too small; otherwise, the strong nearfield coupling between neighboring antennas would perturb the designed scattering amplitudes and phases , θ and ϕ are the elevation and azimuth angles of an arbitrary direction, respectively. When θ and Based on the 1-bit coding MS, the design principle of the coded MS was analyzed, as shown in Here, As previously mentioned, applying various values of sheet resistances of graphene units at different locations across the MS array results in a reconfigurable coding layout. The different distribution of coding particles leads to diverse functionalities only achieved by one planar structure. In our work, three distinct graphene coding sequences are designed to achieve multiple-beam steering. Here, by changing the sheet resistances of graphene MS units to realize beam reconfiguration. As shown in In order to further verify the beam manipulation ability of the graphene-coded MS antenna, the precise phase distribution at a 30° reflection angle was calculated in MATLAB according to Equation (8), as shown in Based on the proposed 1-bit unit cell and phase design, two 20 × 20 phased 1-bit GBCMs with beams pointing at 0° and 30° are designed with a center frequency of 55 GHz. Moreover, the simulated patterns in the xoz plane under normally incident waves at different frequencies are shown in In this work, we propose a simple structured beam steering MMW antenna using novel GBCM, and its coding state can be manipulated by altering the sheet impedance of graphene instead of bias voltage. There have previously been reported many design schemes for GBCM antennas. Paper ,25,26,27Here, for the first time, the broadband manipulation of MMW waves is acquired by introducing a multifunctional graphene-based coding metasurface (GBCM). The designed structure consists of subwavelength patterned graphene units whose operational statuses can be altered between two digital states of “0” and “1”. Its coding state can be manipulated by altering the sheet impedance of graphene instead of bias voltage. This results in a simplified design structure, providing possibilities for the subsequent fabrication of graphene MS antennas. In this work, the tunable behavior of graphene has led to various functionalities, among which we simulated five different coding sequences, implementing dual-, quad- and single-beam-generated beam deflection (30°), as well as a random coding sequence for radar cross-section (RCS) reduction.


Nontuberculous mycobacterial pulmonary disease (NTMPD) is a chronic lung infection that is challenging to treat, especially refractory NTMPD. Data are limited on the natural history, risk profile, and burden of refractory NTMPD. The objective of this study was to evaluate the burden of illness (BOI) among patients with NTMPD who later develop refractory NTMPD vs those who do not.This was a retrospective analysis using BRR data. Patients treated for NTMPD were included and stratified as refractory  or nonrefractory. Modified bronchiectasis severity index (mBSI) at baseline visit and BOI including comorbid conditions and surgeries at the treatment visit were compared by cross-sectional analysis between the two groups. Longitudinal analysis of BOI focused on exacerbations and hospitalizations in the refractory group at their pre-refractory visit and refractory visit (first visit meeting the refractory definition).A total of 1,064 patients treated for NTMPD were included: 462 (43.4%) refractory and 602 (56.6%) nonrefractory. During the baseline period, the refractory group had more severe bronchiectasis . At the time of treatment, bronchiectasis was more common in the refractory than nonrefractory (96.2% vs 89.7%) group, as were hemoptysis (26.6% vs 16.3%) and GERD (50.5% vs 39.9%). Except BMI, there were no statistically significant differences between the pre-refractory and nonrefractory treatment visits (Table 2). In the refractory group, exacerbations and hospitalizations remained high from the pre-refractory to refractory visit . Oxygen and bronchial hygiene use increased from the pre-refractory to the refractory visit  consistent with increased management of respiratory conditions when not responding to treatment (Table 2).Refractory NTMPD imposes a high disease burden on patients in the BRR. This high burden was present at pre-refractory visits and through the development of refractory status.Timothy R. Aksamit, MD, AstraZeneca: Advisor/Consultant|Baxter International, Hill-Rom: Advisor/Consultant|Insmed Incorporated: Advisor/Consultant|Johnson & Johnson: Advisor/Consultant|Redhill Biopharma: Advisor/Consultant|RespirTech: Advisor/Consultant|Spero Therapeutics: Advisor/Consultant|Zambon: Advisor/Consultant David M. Mannino, MD, AstraZeneca: Advisor/Consultant|GlaxoSmithKline: Advisor/Consultant|Up to Date: Advisor/Consultant Ping Wang, PhD, Insmed Incorporated: Salary|Insmed Incorporated: Stocks/Bonds Mariam Hassan, PhD, B. Pharm, Insmed Incorporated: Salary|Insmed Incorporated: Stocks/Bonds


There has been a lengthy debate about whether salient stimuli have the power to automatically capture attention, even when entirely task irrelevant. Theeuwes  has sugg This account claims that salient stimuli will automatically capture attention unless the attentional window is small. More precisely, if attention is broadly distributed at stimulus onset , feature information from all objects in the display is processed, allowing the most salient object to capture attention. By contrast, if attention is narrowly focused at stimulus onset , only the features at the attended location are fed forward to later processing stages and this will prevent the features of the most salient object from being processed and thereby capturing attention. This account is meant to explain a large number of previous studies that have shown that salient distractors can be ignored under conditions that promote feature-based attentional guidance by using heterogenous search displays. Although the attentional window account is an interesting and creative take on past findings, we find that it implies a model of attention that is implausible and inconsistent with previous results. In the current paper, we will describe two major shortcomings of this account.Theeuwes  outlinesThe idea that attention can be either broadly or narrowly focused is well supported by previous research e.g., , but thiguidance problem: if enough featural information is present to guide attention to the target stimulus, then there should be enough featural information for saliency to be computed and control attention (unless the salient object was being suppressed, as proposed by the signal suppression hypothesis; for a review, see Crucially, if a narrow attentional focus prevents the processing of feature information at unattended locations, then it would not be possible for featural information to guide attention. In other words, if there is enough featural information present to guide attention to items with task-relevant features, then there is certainly enough featural information present to determine that one of the distractors is a highly salient color singleton. Thus, for the attentional window account to be feasible, attention must be focused so narrowly that the first shift of attention after stimulus onset is completely random. Although it may be possible to design a search task that is so difficult that the first shift of attention is completely random, most of the tasks classified as “serial” by Theeuwes  contain was preferentially guided toward search items with target features. This suggests that featural information was available to the attentional system and was not shielded as claimed by the attentional window account. For example, Gaspelin et al. (additional singleton paradigm in which participants searched for a specific shape and were instructed to ignore color singleton distractors (In many previous studies in which Theeuwes  would arn et al. , Exp. 3 tractors . The firtractors . It doesOther approaches have also demonstrated similar evidence of attentional guidance toward the target shape in search tasks with heterogenous distractors. For example, Gaspelin et al. , Exp. 2 Even if we ignore the evidence that the attentional window was broad enough for substantial feature processing in previous experiments, the attentional window account does a poor job of accounting for the presence or absence of capture in prior studies. A key tenet of the attentional window account is that easy search tasks that encourage a “parallel” search will yield attentional capture, and difficult search tasks that encourage a “serial” search will not yield capture. A natural question is therefore whether this prediction fits the general pattern of results observed in the existing attentional capture literature.For example, a distinction between easy and difficult search tasks cannot account for the lack of capture by salient-but-irrelevant cues in the spatial-cueing paradigm e.g., . In thisThe lack of capture in the spatial-cueing paradigm is problematic for the attentional window account because these tasks typically use easy search displays that would seem to encourage a diffuse attentional window. For example, Lien et al.  measuredAn easy-difficult search dichotomy also does not explain strategic changes in performance in the additional singleton paradigm. Leber and Egeth  directlyA parallel-serial dichotomy also cannot explain the discrepant findings between bottom-up theories of capture and the signal suppression hypothesis (as suggested by exactly the same stimuli as Wang and Theeuwes (Theeuwes  now suggTheeuwes  and stilAn easy-difficult dichotomy also does not easily explain learned suppression effects, whereby individuals learn to avoid capture by salient distractors on the basis of their specific feature values . For exaIn sum, the attentional window account is unrealistic for two main reasons. First, it presumes that the attentional window is so narrowly focused at search display onset that feature processing is suppressed outside the window. However, this seems unlikely on theoretical grounds, and it is also inconsistent with clear evidence of feature-based guidance. Second, it presumes that the degree of capture should vary across studies with the difficulty of the search task. However, this does not fit the pattern observed in prior research. In our view, a more parsimonious model of attention would simply accept that salient distractors can sometimes be prevented by feature-based attentional control settings e.g., .


Amyotrophic lateral sclerosis (ALS) is a multi-systemic, incurable, amyloid disease affecting the motor neurons, resulting in the death of patients. The disease is either sporadic or familial with SOD1, C9orf72, FUS, and TDP-43 constituting the majority of familial ALS. Multi-omics studies on patients and model systems like mice and yeast have helped in understanding the association of various signaling and metabolic pathways with the disease. The yeast model system has played a pivotal role in elucidating the gene amyloid interactions. We carried out an integrated transcriptomic and metabolomic analysis of the TDP-43 expressing yeast model to elucidate deregulated pathways associated with the disease. The analysis shows the deregulation of the TCA cycle, single carbon metabolism, glutathione metabolism, and fatty acid metabolism. Transcriptomic analysis of GEO datasets of TDP-43 expressing motor neurons from mice models of ALS and ALS patients shows considerable overlap with experimental results. Furthermore, a yeast model was used to validate the obtained results using metabolite addition and gene knock-out experiments. Taken together, our result shows a potential role for the TCA cycle, cellular redox pathway, NAD metabolism, and fatty acid metabolism in disease. Supplementation of reduced glutathione, nicotinate, and the keto diet might help to manage the disease. Amyotrophic lateral sclerosis (ALS) is a complex multi-systemic disease associated with the loss of motor neurons in the brain stem, spinal cord, and motor cortex . NeurodeTranscriptomic, proteomic, and metabolomic analyses have been carried out on ALS patients and transgenic model systems with SNP in SOD1, C9orf72, FUS, and TDP-43 to understand the disease . MutatioThe TDP-43 gene is located on chromosome number 18. The protein is made up of 414 amino acids . Most ofC. elegans, drosophila, zebrafish, and mice could capture many aspects of ALS pathogenesis. Omic studies like transcriptomic, proteomic, and metabolomic studies on the model systems show deregulated pathways associated with the disease  were used for pathway enrichment analysis. Pathway enrichment analysis was carried out using Enrichr [Analysis date: January 2022–March 2022 (www.maayanlab.cloud/Enrichr)]  ,40,41, aion 4.0)  was usedRT Polymerase Chain Reaction (RT-PCR): RT-PCR was performed to validate the results obtained from transcriptomics. Three genes that were expressed in the TCA cycle, signaling, and fatty acid metabolism  were identified. cDNA synthesis was performed using the Thermo Scientific cDNA synthesis kit (#AB-1453/B) as per the manufacturer instructions. PCR was performed using 384 well format Quant Studio 5 PCR machine. Primers were designed using Primer Blast, and synthesized by Barcode Biosciences . ALG9 was used as a housekeeping gene. The primer sequences used for the study are presented below : RNA sequencing results and Metabolomic results were integrated using Metabo-analyst  [ion 5.0) . A list GEO (Gene Expression Omnibus) data analysis: Gene Expression dataset pertaining to mice motor neuron expressing TDP-43 aggregates and its mutant were identified from Gene Expression Omnibus Database (GSE111775) [GSE124439) [E111775) . ApproprE111775) ,44,45,46E124439)  was alsoE124439) .Comparative analysis: Commonality analysis was carried out between the GEO-derived dataset of TDP-43-A315T, ALS Post-mortem cortex sections, and the integrated analysis pathway results of the TDP-43-Q331K Mutant. A Venn diagram was plotted using Venny . Commonality analysis was also carried out between literature-derived metabolomic datasets of human Cerebrospinal fluid (CSF) [www.bioinfogp.cnb.csic.es/tools/venny, Version 2.1, Analysis date: January 2022–March 2022).id (CSF) , Mice TDid (CSF) , and pooExperimental validation (Metabolite addition experiments and knock-out studies): Metabolite addition experiments have been carried out on various model systems [Saccharomyces cerevisiae transformed with TDP-43 and its mutants. Transformed cells were grown in URA-YNB-Dextrose medium for 12 h. The cells were then pelleted, washed, and grown in URA-YNB-Raffinose medium. After 7 h, the cells were again pelleted, washed, and grown in galactose medium. The cells were treated with different metabolites based on the transcriptomic and metabolomic results. The cells were allowed to grow in galactose medium for 8 h. After 8 h, fluorescence imaging was carried out as stated before. Samples were randomized for imaging studies. Further, fluorescence quantification was carried out using Icy software [icy.bioimageanalysis.org, version 2.1.0.0). The results were analyzed using Microsoft Excel 2019. The yeast knock-outs library from Dharmacon  was used for the study’s yeast knock-outs. Yeast knock-outs were transformed and imaged in a similar manner as described previously. Fluorescence quantification was performed using Icy software  and Microsoft Excel 2019. systems . We carrsoftware   SDS containing TBS buffer was used to assemble the bio-dot device . 150 µg of protein was heated at 100 °C for 10 min and was loaded in the wells. A vacuum was applied to enhance the binding of the protein with the membrane. After 10 min, the membrane was removed. The blot was incubated in 20 mL of TBST overnight at 4 °C with 2 µL of mouse anti-GFP antibody . On a gel rocker, the blot was washed twice with 25 mL of TBST. The cleaned blot was incubated for 2 h at room temperature with 1 µL of goat anti-mouse IgG-HRP antibody  in 25 mL of TBST. The ECL substrate  and Syngene Gbox F3 were used to create the blot. The blots were quantified using Icy software  and Microsoft Excel 2019.rotocols . The yearotocols . The bloS. cerevisiae after transformation with respective plasmids  of ≤20% after normalization with internal standards were used for further analysis. A total of 36 significant differential metabolites were obtained , and an adjusted of ≤0.05 A. Of theof ≤0.05 A. To furof ≤0.05 B. Our RTof ≤0.05 B. Havingof ≤0.05 . Our anaof ≤0.05 C. The deof ≤0.05 C. The siof ≤0.05 D. The siof ≤0.05 D. Taken Integrated analysis of transcriptomic and metabolomic datasets from the yeast model of TDP-43 aggregation was performed using Metaboanalyst, as described in the methods section. The analysis yielded 18 significantly deregulated metabolic pathways . Our anaS. cerevisiae expressing wild type or TDP-43 mutants . The metabolic profile of TDP-43 mutants (G294A and M337V) was compared to TDP-43 wild type. For G294A and M337V, a total of 57 and 21 significant differential metabolites were obtained  exhibited deregulation of metabolic pathways which are similar to those expressing Q331K, we carried out targeted metabolomic analysis of obtained A. Pathwaobtained . The metobtained B. Since Commonality analysis for overlapping pathways yielded 8 pathways as common among all the TDP-43 mutants, while 16 pathways were common between Q331K and G294A . The TDPWe further asked if our findings in the yeast model are of relevance to ALS. For this, we carried out an analysis of transcriptomic datasets of mice models of ALS and ALS patients from the GEO database. The datasets used are the TDP-43 A315T expressing transgenic mice motor neurons and cortex of ALS patients. GSEA analysis of motor neuron dataset from mice model of ALS showed deregulation of pathways like primary immunodeficiency, neuroactive ligand-receptor interaction, Parkinson’s disease, glycerolipid metabolism, spliceosome, oxidative phosphorylation, and ribosome. The detailed results are provided in the supplementary section . SimilarCommonality analysis of deregulated pathways across ALS patients, yeast and mice models of ALS was performed. Two pathways (ribosome and oxidative phosphorylation) were common to yeast, mice, and human datasets A. A totaFurther, the metabolites obtained from literature for TDP-43-A315T transgenic mice motor neurons were analysed using Metaboanalyst, and the results are provided . MetabolOur integrative analysis of transcriptomics and metabolomics shows deregulation of the TCA cycle. Similarly, the TDP-43 mice model brain and ALS patient post-mortem cortex transcriptomic data also displayed deregulation of the TCA cycle. Metabolic addition experiments using succinate and alpha-ketoglutarate show significantly elevated protein aggregates . ConsistThe transcriptomic data from our yeast studies and the mice brain data from the GEO database show deregulation of glutathione metabolism. Supplementation of reduced glutathione significantly reduced amyloidogenesis, while oxidized glutathione led to an insignificant increase in amyloidogenesis . MetabolFurther, we carried out filter retardation assay to quantify the wild type or mutant TDP-43 in the total protein, as well as in the soluble and insoluble fractions, as described in the methods section. In particular, we had used the wild type and mutant TDP-43 which are untreated or treated with butyric acid or palmitic acid. This will help to correlate the changes in total protein, and soluble and insoluble protein of TDP-43, as well as validate that the observations of fluorescence imaging experiments are reliable. Consistent with fluorescence imaging data, our analysis showed significantly higher total protein in the mutants compared to the wild type. Similarly, the amount of mutant TDP-43 in both soluble and insoluble fractions were significantly higher compared to the wild type C. We furIn this study, we used the yeast model of TDP-43 aggregation to understand the transcriptomic and metabolomic changes as a consequence of mutation in TDP-43. The relevance of deregulated pathways obtained for yeast were compared with those obtained for motor neurons from the TDP-43 mice model of ALS (GSE111775) and ALS patients (GSE124439). Further, the role of deregulated transcriptomic and metabolomic pathways on the aggregation of TDP-43 in the yeast model was studied. Furthermore, metabolic addition experiments and yeast knock-out of specific genes in deregulated pathways were used to validate their role in the disease progression. Consistent with previous reports, in the TDP-43 expressing yeast model elevated TDP-43 aggregation was observed in the mutant  compared to the wild type, which also corroborates with results of the filter retardation assay. Previous studies have also shown increased severity and aggregation in TDP-43 mutants compared to the wild type TDP-43 expressing cells .Transcriptomic analysis of yeast expressing mutant TDP-43 shows enrichment of peroxisome, ribosomes, metabolism of different amino acids, nucleotide metabolism, fatty acid metabolism, and MAPK signalling. Transcriptomic analysis of zebrafish transgenic for TDP43 (G348C) showed changes in the levels of many differentially expressed genes that were related to neuromuscular disorders, including ALS and muscular dystrophy . The genThe increased ROS in TDP-43 mutants has been reported previously . PreviouThe integrative analysis of transcriptomic and metabolomics shows that the transcriptomic changes translate into metabolic changes in the yeast model of TDP-43. Previous studies have shown increased glycolysis and metabolites belonging to the TCA cycle in ALS . To corrFurther, transcriptomic analysis of yeast and mice models of ALS shows deregulation of the glutathione pathway and oxidative stress, while transcriptomic and metabolomic analysis shows deregulation of the pentose phosphate pathway. Previous studies have also shown oxidative stress and ROS in the yeast model of TDP-43 aggregation . SupplemThe current study focuses on an integrative multi-omic analysis of the yeast model of TDP-43 aggregation for the identification of deregulated pathways, and comparing it with patients and other model systems. Further, the role of these deregulated pathways in amyloid formation in the yeast model of TDP-43 aggregation is discerned using metabolic addition experiments and gene knock-outs. Metabolomic analysis of TDP43 wild type or different mutants expressed in the yeast model of TDP-43 aggregation shows deregulation of metabolic pathways. These pathways include nicotinate and nicotinamide metabolism, glutathione metabolism, and metabolism of various amino acids. Transcriptomic analysis of the yeast model of TDP-43 aggregation (Q331K) shows deregulation of the TCA cycle, oxidative phosphorylation, peroxisome, fatty acid degradation, metabolism of amino acids. The integrated transcriptomic and metabolomic analysis shows glutathione pathway, fatty acid degradation, metabolism of various amino acids. The transcriptomic and metabolomic analyses displayed considerable concordance. Further, pathways from transcriptomic datasets of the yeast model were compared with those obtained from ALS patients and mice models of ALS from the GEO database. Our analysis shows that the TCA cycle, oxidative phosphorylation, as well as protein processing and degradation are common to different datasets. Validation of the results using the yeast model showed short-chain fatty acids significantly abrogated amyloid formation, while long-chain fatty acids significantly ameliorated it. Furthermore, results of the filter retardation assay of short-chain and long-chain fatty acids treated and untreated wild type and mutant cells corroborated well with the fluorescence image data. Similarly, reduced glutathione and succinate reduced protein aggregation. Consistent with this, KO of MDH2 and KGD10 reduced protein aggregation. Taken together, our results show deregulated pathways modulate amyloid formation in the yeast model of TDP-43 aggregation. The results show a potential use for a short-chain fatty acid-containing keto diet, and antioxidants such as glutathione and nicotinate in managing and improving the living conditions of ALS patients.


It is unknown how the patterns of negative and positive attentional biases in children predict fear of COVID-19, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. The study identified profiles of negative and positive attentional biases in children and examined their association with emotional symptoms during the COVID-19 pandemic.264 children (girls: 53.8% and boys: 46.2%) of 9–10 years born in Hong Kong or mainland China from a primary school in Shenzhen, People’s Republic of China were involved in a two-wave longitudinal study. Children completed the COVID-19 Fear Scale, the Revised Child Anxiety and Depression Scale, and the Attention to Positive and Negative Information Scale to measure fear of COVID-19, anxiety and depression symptoms, and negative and positive attentional biases in classrooms. After six months, they completed the second assessment of fear of COVID-19, anxiety symptoms, and depression symptoms in classrooms. Latent profile analysis was conducted to reveal distinct profiles of attentional biases in children. A series of repeated MANOVA was performed to examine the association of profiles of attentional biases to fear of COVID-19, anxiety symptoms, and depression symptoms across 6 months.Three profiles of negative and positive attentional biases were revealed in children. Children with a “moderate positive and high negative attentional biases” profile had significantly higher fear of the COVID-19 pandemic, anxiety symptoms, and depression symptoms than children with a “high positive and moderate negative attentional biases” profile. Children with a “low positive and negative attentional biases” profile were not significantly different in fear of COVID-19, anxiety symptoms, and depression symptoms than those with the other two profiles.Patterns of negative and positive attentional biases were related to emotional symptoms during the COVID-19 pandemic. It might be important to consider children's overall patterns of negative and positive attentional biases to identify children at risk of higher emotional symptoms.The online version contains supplementary material available at 10.1186/s13034-023-00594-y. The COVID-19 pandemic might lead to an increase in the rate of emotional symptoms among children . PreviouLongitudinal studies that examine how the profiles of attentional preferences affect emotional symptoms during the COVID-19 pandemic are particularly scarce. Considering the importance of attentional bias in children’s development of internalizing symptoms, more research is needed to investigate how children’s overall patterns of attentional biases might affect the development of fear, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. These findings would add to the literature what were the overall patterns of negative and positive attentional biases in children during the COVID-19 pandemic, and how these patterns of negative and positive attentional biases were associated with emotional symptoms during the COVID-19 pandemic. Such findings might also add to our understanding of whether psychologists should consider both negative and positive attentional biases simultaneously when investigating the development of emotional symptoms in children, especially during the COVID-19 pandemic.To address these research gaps, the present study aimed to reveal profiles of attentional biases and to examine how these profiles were associated with the development of fear of COVID-19, anxiety symptoms, and depression symptoms in children during the COVID-19 pandemic.Attentional bias refers to the cognitive tendency to shift attention toward negative or positive stimuli. Based on the cognitive models, negative attentional bias is the vulnerability factor for the development of anxiety symptoms and depression symptoms in children , 33. ResAttentional training that targeted negative attentional bias has shown efficacy in modifying self-esteem as an indicator of depression symptoms in 108 participants over 17 years old with mild or minimal depression symptoms  [During the COVID-19 pandemic, children around the age of 14.23 years (N = 195) recruited from the Netherlands might experience strong fear of COVID-19 infection which was the result of communication of threatening COVID-19-related information with parents . A meta-Profiles of negative and positive attentional biases depicted overall patterns of attentional preferences to negative and positive stimuli. Although negative attentional bias might be the opposite of positive attentional bias in terms of the affective valence of preferred stimuli, people might have low negative attentional bias and low positive attentional bias simultaneously. The presence of high negative and high positive attentional biases might predict lower emotional symptoms than those with high negative and low positive attentional biases. For example, a study with an unselected community sample of 270 adults (the mean age of 22 years) from the undergraduate student population and community forum has shown that attentional bias toward threats was linked to high self-report anxiety symptoms only in individuals with a tendency to attend away from positive information . Latent The current research recruited a sample of children from a primary school. The study aimed to 1) reveal distinct profiles of negative and positive attentional biases in children during the COVID-19 pandemic. Also, the study aimed to 2) reveal the relationship of profiles of attentional biases to fear of COVID-19, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. The majority of existing studies have only investigated either negative attentional bias or positive attentional bias separately. No previous studies have used the latent profile analysis to investigate the effect of the overall patterns of attentional biases on children’s emotional symptoms during the COVID-19 pandemic. This study would be the first study that took a holistic approach to reveal profiles of attentional biases in children. Also, it would provide the first evidence from a longitudinal study for how profiles of attentional biases would affect fear of COVID-19, anxiety symptoms, and depression symptoms during the COVID-19 pandemic.Based on the previous evidence, distinct profiles of attentional biases would be revealed which might include profiles of “low negative and positive attentional bias”, “low negative and high positive attentional bias”, “high negative and low positive attentional bias” and “high negative and positive attentional bias”. Compared to the “high negative and positive attentional bias” profile, the “low negative attentional bias and high positive attentional bias” profile would predict low fear, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. While compared to the “low negative and positive attentional bias” profile, the “high negative attentional bias and low positive attentional bias” profile would be related to more fear, anxiety symptoms, and depression symptoms in children during the COVID-19 pandemic.The study initially recruited a sample of middle-class 322 children from a primary school in Shenzhen, PRC. Children had similar social economic status and education levels. They were from middle-class families and had the same grade at the same school.Convenient sampling was used to recruit participants. First, public schools where children were normal Chinese students from middle-class families were selected. This population was chosen because the middle class is rapidly growing, and takes a significantly large proportion of the population in China . Then, iboys = 124 and N girls = 138) were input into analyses.Regarding gender distribution, 53.8% (N = 171) of participants were girls, and 46.2% (N = 147) were boys; three participants did not indicate their gender. The average age of the sample was 9.54 years, ranging from 9 to 10 years (SD = 0.505 years). Exclusion criteria were (1) not able to fluently speak and understand simplified Chinese, (2) having a diagnosis of psychological disorders or developmental disorders, and (3) birthplace outside of mainland China. 50 children were excluded from the study because they failed to meet the inclusion criteria. 8 children who failed to complete one of the self-report assessments were excluded from the research. Altogether, 58 students were excluded from the study. Consequently, data from 264 children . First, researchers assigned the informed consent and assent forms to students. After collecting the informed consent and assent forms from students, children completed the Attention to Positive and Negative Information Scale for approximately 30 min in the classrooms. Researchers answered questions regarding the scale from students in the classrooms.The present study was conducted from November 16th to November 30th, 2020, when the COVID-19 pandemic was ongoing. The prevention measures, including social distancing, were implemented during the period. Teachers in the primary school helped researchers disseminate informed consent forms and assent forms to parents and students. The signed informed consent and assent forms were collected one day before the date of data collection in the classrooms. Only children who signed the assent forms and obtained the signed informed consent forms from their parents participated in the study. On the data collection date, research assistants assigned a set of questionnaires to students during classes. The participants were informed that they could ask questions and withdraw from the study at any time without any consequence. The assessment of attentional biases was conducted concurrently with the first assessment of emotional symptoms at time one (T1) and before the second assessment of emotional symptoms at time two (T2). At time one, students completed a set of questionnaires for children to assess their negative and positive attentional biases, fear of COVID-19, anxiety symptoms, and depression symptoms for approximately 45 min in the classrooms. After approximately six months, students completed the questionnaire to assess their fear of COVID-19, anxiety symptoms, and depression symptoms at time two in the classrooms for approximately 45 min. In each assessment, a teacher and researcher were present to answer students’ questions and ensure the safety of students.The eight-item Chinese version of the Attention to Positive and Negative Information Scale (CAPNIS) was administered to children , 23. Thehttp://www.childfirst.ucla.edu/Resources.html). RCADS is a wildly used scale for childhood anxiety symptoms and depression symptoms. The 47 items of the scale can be grouped into the Anxiety Disorders and the Major Depression sub-scales, rated on a Likert scale from 0 to 3 . The scale of Anxiety Disorders has 37 items , and the scale of Major Depressive includes 10 items . An anxiety symptoms score and a depression symptoms score were calculated by summing up scores of the relevant items on the subscales of Anxiety Disorder and Major Depression, respectively. The scales showed excellent reliability with the current data .The Chinese version of the Revised Child Anxiety and Depression Scale  was usedFear of COVID-19 was measured by the COVID-19 Fear Scale (CFS). CFS was adapted from the Chinese version of the 18-item SARS Fear Scale . When adapting the CFS scale, three items were deleted from the original scale, because these items were only related to healthcare workers . Also, instead of SARS, participants were asked about their response to the COVID-19 pandemic in the COVID-19 Fear Scale.Participants were asked to rate on a 4-point Likert scale how much the statement is true in the face of the COVID-19 pandemic. 0 indicates "definitely false" and 3 indicates "definitely true." The sample item includes "Fear that I will be infected." Three items were excluded from CFS  because of their inapplicability to students. A CFS score was obtained by summing up scores from each item. The higher the CFS scores, the higher the fear of COVID-19. The scale demonstrated good internal consistency in the current study .Exploratory factor analysis was first performed to examine the structural validity of the Attention to Positive and Negative Information Scale with data from the preliminary study , the adjusted Lo–Mendell–Rubin likelihood ratio test (Adj. LMR), as well as the Bootstrapped Likelihood ratio test (BLRT), showed that a model with k number of groups was significantly better than a model with k-1 groups [Descriptive statistics were presented. Two-tailed Pearson’s partial correlation was investigated using SPSS version 22 to understand the association among psychological variables. Independent t-tests examined the gender differences in the psychological variables. Latent profile analyses were subsequently conducted to reveal profiles of negative and positive attentional biases in children with MPlus 7.4. With maximum likelihood estimation with robust standard error (MLR), heterogeneous profiles of attentional biases were revealed. The number of groups for the model was increased until the model was not significantly better than the model with one fewer group. Smaller values of the Bayesian Information Criterion (BIC) and Akaike information criteria (AIC), as well as the higher value of entropy, suggested a better model fit . The sig1 groups . Next, C1 groups , 18. The t (260) = 1.48, p = 0.14, positive attentional bias, t (260) = − 0.04, p = 0.97, and depression symptoms at T1, t (260) = 1.41, p = 0.16. Girls showed significantly higher anxiety symptoms at T1, t (260) = 3.13, p < 0.005, higher anxiety symptoms at T2, t (260) = 2.92, p < 0.005, and higher depression symptoms at T2, t (260) = 2.17, p < 0.05, than did boys. Also, girls were significantly higher in fear of COVID-19 at T1, t (257) = 2.12, p < 0.05, than boys. Girls were significantly higher in fear of COVID-19 at T2, t (256) = 2.92, p < 0.005. Refer to Table An independent t-test was performed with 262 participants to examine the difference between girls and boys in positive and negative attentional biases, fear of COVID-19, depression symptoms, and anxiety symptoms assessed at time one (T1) and time two (T2). Results revealed that girls and boys didn’t significantly differ in negative attentional bias,r = − 0.01, p = 0.82, negative attentional bias, r = 0.04, p = 0.57, anxiety symptoms at T1, r = − 0.04, p = 0.52, depression symptoms at T1, r = − 0.03, p = 0.61, anxiety symptoms at T2, r = − 0.03, p = 0.60, and depression symptoms at T2, r = − 0.02, p = 0.76.The Pearson’s partial correlation, controlling for gender revealed that age was not significantly associated with positive attentional bias, Higher anxiety symptoms at T1 and T2 were significantly related to higher depression symptoms at T1 and T2. They were also significantly associated with higher negative and lower positive attentional biases. Depression symptoms at T1 and T2 were positively and significantly associated with negative attentional bias; depression symptoms at T1 and T2 were negatively and significantly related to positive attentional bias. Higher fear of COVID-19 at T1 and T2 was associated with significantly higher anxiety symptoms and depression symptoms at T1 and T2. Also, higher fear of COVID-19 at T1 and T2 was significantly related to higher negative attentional bias. Higher fear of COVID-19 at T1 was significantly associated with lower positive attentional bias. Higher fear of COVID-19 at T2 was not significantly related to lower positive attentional bias. Higher anxiety symptoms at T1 were significantly related to higher anxiety symptoms at T2; higher depression symptoms at T1 were significantly related to higher depression symptoms at T2. Fear of COVID-19 at T1 was significantly related to higher fear of COVID-19 at T2. Negative attentional bias was not significantly associated with positive attentional bias. Refer to Table p values of VLMR, Adj. LMR and BLRT were insignificant for the four-class model, which suggested that the four-class model was not significantly better than the three-group model. Thus, the three-class model showed the best model fit.The fit indices for models with one to five groups were examined with a latent profile analysis , and was named as “low positive and negative attentional biases” profile . Class two had the highest positive attentional bias and moderate negative attentional bias , which was accordingly defined as the “high positive and moderate negative attentional biases” profile . Class three had a moderate positive attentional bias and high negative attentional bias , which was named the “moderate positive and high negative attentional biases” profile . Figure Class one had the lowest level of negative and positive attentional biases (Mean χ2 (2) = 0.23, p = 0.89, birthplace, χ2 (2) = 1.14, p = 0.57, and age groups (9 & 10 years), χ2 (2) = 1.24, p = 0.54. Refer to Table The three latent profiles were not significantly different in terms of gender, F  = 5.66, p < 0.001. The effect size was small, partial η2 = 0.042. Power for the effect of profiles on anxiety symptoms was as high as 0.86.The Bonferroni post hoc test showed that children with a “moderate positive and high negative attentional biases” profile had significantly higher anxiety symptoms  than children with a “high positive and moderate negative attentional biases” profile . Children with a “low positive and negative attentional biases” profile  were not significantly different in anxiety symptoms level from children with the other two profiles  = 6.43, p < 0.01. The effect size was small with partial η2 = 0.047. The between-subject effect had high calculated power of 0.90. Bonferroni post hoc test showed that children with a “moderate positive and high negative attentional biases” profile  had significantly higher depression symptoms than children with a “high positive and moderate negative attentional biases” profile . Children with a “low positive and negative attentional biases” profile  were not significantly different in depression symptoms from children with the other two profiles  = 6.43, p < 0.05. The effect size was small, partial η2 = 0.031. The calculated power for the effect of profiles on fear of COVID-19 was 0.72. The Bonferroni post hoc test showed that children with a “moderate positive and high negative attentional biases” profile  demonstrated significantly higher fear of COVID-19 than children with a “high positive and moderate negative attentional biases” profile . Children with a “low positive and negative attentional biases”  profile didn’t significantly differ in fear of COVID-19 from children with “moderate positive and high negative attentional biases” and “high positive and moderate negative attentional biases” , “high positive and moderate negative attentional biases” profile (38.5%), and “moderate positive and high negative attentional biases” profile (57.2%). Profiles of attentional biases showed a significant relationship with emotional symptoms over six months during the COVID-19 pandemic; the “moderate positive and high negative attentional biases” profile was related to significantly higher anxiety symptoms and depression symptoms than “high positive and moderate negative attentional biases” profile. The “low positive and negative attentional biases” profile was not significantly different from the other two profiles in emotional symptoms. Consistent with the findings, previous studies which measured negative attentional bias by behavioral tasks also found a positive association between negative attentional bias and emotional symptoms. , 16. TheMoreover, during the COVID-19 pandemic, negative attentional bias was related to higher health anxiety and higher COVID-19-related anxiety symptoms , 4. ThisThese were the first results identifying children’s patterns of attentional biases during the COVID-19 pandemic and their relationship with emotional symptoms. With latent profile analysis and the longitudinal design, the results added to the literature that it was important to consider negative and positive attentional biases simultaneously and to use profiles of attentional biases to predict the development of emotional symptoms during the COVID-19 pandemic. Evidence might provide significant implications for the cognitive model of emotional symptoms, suggesting that negative attentional bias was as important as positive attentional bias, and thus the overall patterns of negative and positive attentional bias might be more important than negative attentional bias alone.The current study revealed three profiles of positive and negative attentional biases in children during the COVID-19 pandemic. Over half of the children showed a “moderate positive and high negative attentional biases” profile (59.5%), and only a smaller number of children had a “low positive and negative attentional biases” profile (3.8%) or “high negative attentional bias and moderate positive attentional bias” profile (36.7%). Consistent with previous research, most children’s attentional bias towards negative stimuli tended to be moderate or high during the COVID-19 pandemic .


The diversity and primary productivity in the Arctic ecosystem are rapidly changing due to global warming. Microorganisms play a vital role in biogeochemical cycling. However, the diversity of planktonic microorganism communities in the Laptev Sea, one of the most important marginal seas of the Western Arctic Ocean, have not been studied sufficiently in depth. The diversity and community structure of the planktonic microorganisms in the surface water were investigated at 20 stations on the Lena River flowing into the Laptev Sea. Multivariate statistical analyses demonstrated clear spatial patterns in the α diversity and community structure for microorganisms under different salinity levels. Co-occurrence networks of microbial communities revealed that spatial variation promoted differentiation of the characteristics and stability of microbial networks in the Laptev Sea. Contrary to expectations, abundant taxa were found to not have a large influence on the stability and resilience of microbial interactions in the region. On the contrary, less-abundant taxa were found to have far greater influence. The stability and resilience of the prokaryotic and microeukaryotic networks in the Lena River estuary and the continental shelf provided valuable insights into the impact of freshwater and land inflow disturbances on microbial assemblage. Overall, these results enhance our understanding of the composition of microbial communities and provide insights into how spatial changes of abundant versus rare species alter the nature and stability of microbial networks from the Lena River estuary to the Laptev Sea. In addition, this study explored microbial interactions and their ability to resist future disturbances.IMPORTANCE The regime of the Laptev Sea depends closely on the runoff of the Lena River. Microorganisms are essential components of aquatic food webs and play a significant role in polar ecosystems. In this study, we provided a basic microbial data set as well as new insights into the microbial networks from the Lena River estuary to the Laptev Sea, while exploring their potential to resist future disturbances. A comprehensive and systematic study of the community structure and function of the planktonic microorganisms in the Laptev Sea would greatly enhance our understanding of how polar microbial communities respond to the salinity gradient under climate warming. While the earth has warmed by ~0.8°C since the late 19th century, the Arctic region has warmed by 2 to 3°C . The inpMany studies conducted in the Laptev Sea area over the past 2 decades have focused on assessing the impact of the river runoff on the Arctic Ocean ecosystem. Most previous studies have focused on the community composition, distribution, and abundance of benthic fauna , zooplanPrevious studies have shown that microbial interactions may be more important than other environmental variables in determining the structure of microbial communities , 14. FurThis study aimed to increase our understanding of the structure of prokaryotic and microeukaryotic communities, as well as the effects of terrestrial inflow on the assembly of microbial communities and interactions between microbes in the surface waters from the Lena River estuary to the Laptev Sea. Specifically, we (i) characterized the composition and distribution of the planktonic microbial community (including both prokaryotes and microeukaryotes), (ii) evaluated the importance of deterministic and stochastic processes in shaping the community structure and the response of the microbial community to elevated salinity and temperature, and (iii) explored the interactions among the microbes and identified the main factors affecting community structure and microbial interactions in the Laptev Sea.Surface water temperatures were higher in the Lena River estuary (stations LF01 to LF07) and the nearshore (stations LF08 to LF11) than in the continental shelf (LF12 to LF20 [offshore]) . The salProteobacteria. Numerous sequences affiliated with Actinobacteria and Cyanobacteria were detected in the estuary and nearshore . However, the diversity of microeukaryotes was not significantly different among the three regions (P > 0.05) (https://github.com/qian8341/SUPPLEMENTAL-MATERIAL]). The Bray-Curtis dissimilarity index revealed that β diversity was highest for the prokaryotic and microeukaryotic communities in samples from the offshore region .The Shannon-Wiener diversity of prokaryotes was significantly lower in samples from the offshore region than in those from the estuary and transitional regions (P < 0.05) . The abundant prokaryotic OTUs were negatively correlated with salinity, and a small number of prokaryotic OTUs were positively correlated with temperature. The abundant microeukaryotic OTUs were positively correlated with salinity but negatively correlated with temperature . The effect of salinity on the α diversity of prokaryotes was stronger than that of temperature  were significantly correlated with salinity, and a few of the abundant OTUs were significantly correlated with temperature (perature .12] = 0.981) separated microeukaryotic communities in the estuary and nearshore regions from those in the offshore region, and the second canonical axis (δ22 = 0.838) separated microeukaryotic communities in the nearshore from communities in the estuary and offshore regions (P < 0.005). Unlike microeukaryotes, prokaryotic communities were divided into two groups. The first canonical axis (δ22 = 0.978) separated the prokaryotic communities in the offshore region from those in the estuary as well as the nearshore. However, the second canonical axis did not separate the prokaryotic communities in the estuary from those in the nearshore (P < 0.05). However, there was no significant difference between the prokaryotic communities in the estuary and transitional region (P > 0.05).Discrimination of the community structures was plotted using canonical analysis of the principal coordinates (CAP) on the Bray-Curtis similarity matrices from square root-transformed OTU abundance data . The CAP regions . A permuearshore . A PERMAR2 values  indicated that prokaryotic community assemblages were well described by neutral-based models and that the stochastic processes made important contributions to shaping the assembly of prokaryotic communities in this region. The relative contribution of stochastic processes to the assembly of microeukaryotic communities was lower than it was for prokaryotic communities, as only 27.8, 5.5, and 29.1% of the community variance for the estuary, offshore, and entire study region, respectively, could be attributed to the stochastic processes (m) was higher for the prokaryotic community (0.213) than it was for the microeukaryotic community (0.037).The Sloan neutral community model (NCM) was used to identify the relative importance of stochastic processes in the assembly of the prokaryotic and microeukaryotic communities . The R2 rocesses . The immThe dispersal-niche continuum index (DNCI) values of prokaryotes and microeukaryotes were −28.354 and −39.394, respectively, which indicated that dispersal assembly was dominant for microbial communities. Models with both sites and species constrained showed the smallest deviation from our data, while those constraining only one of the sites showed the highest deviation . That mehttps://github.com/qian8341/SUPPLEMENTAL-MATERIAL]). The topological properties of this network are shown in Table S2. The average path length, diameter, clustering coefficient, and modularity of this network were 6.047, 170, 690, and 0.773, respectively. These topological properties indicate the existence of ecological interactions between prokaryotes and microeukaryotes from the Lena River estuary to the Laptev Sea and that ecological niches in this region are shaped by habitat partitioning/sharing.Co-occurrence networks were constructed based on the species interaction relationships to determine the co-occurrence patterns of planktonic microbial communities in surface water from the Lena River estuary to the Laptev Sea . The resThe aforementioned thresholds were applied to obtain prokaryotic and microeukaryotic networks in the region under consideration (f) to remove the top 10 or 20 nodes  with the highest betweenness centrality , the most relative abundance, and the highest degree (number of connections) was analyzed. Although the number of components and the value of f were different from those in the original networks in the case in which nodes with the highest betweenness centrality were removed, few changes were observed when nodes with the highest degree or most relative abundance were removed (Table S2). This suggests that the removal of nodes with the highest betweenness centrality can affect the stability of microbial networks more strongly than the removal of the nodes with the most abundance or the highest degrees. The strong correlations between the betweenness centrality and the degree of node normalization mean that the nodes with high betweenness centrality (gatekeepers) also tended to be of a high degree. Thus, once the gatekeepers were removed, other hub species could replace them and connect different compartments, holding the network together, which could help maintain the stability of the network and enhance its resistance to disturbance. The top 10 or 20 taxa were selected from a ranking based on abundance, degrees, and betweenness centrality, and are listed in Table S4 at the GitHub URL above. The high-betweenness-centrality nodes of the meta-network belonged to Alveolata, Opisthokonta, Stramenopiles, Archaeplastida, Alphaproteobacteria, Kiritimatiellaeota, Cyanobacteria, and Gammaproteobacteria. The high-betweenness-centrality nodes of the prokaryotic network belonged to Actinobacteria, Alphaproteobacteria, Cyanobacteria, Gammaproteobacteria, and Kiritimatiellaeota. Archaeplastida, Alveolata, and Stramenopiles were the nodes with the highest betweenness centrality in the microeukaryotic network. These nodes have a significant effect on the structure and persistence of microorganisms, including the prokaryotic and microeukaryotic communities from the Lena River estuary to the Laptev Sea because they hold the network together. While a few of these nodes were abundant species, the abundance of most of these species was low.The response capability of network fragmentation (f) for the removal of the top 10 or 20 nodes with the highest betweenness centrality  and Gammaproteobacteria  and decreased in Actinobacteria  and Cyanobacteria  with increasing salinity levels  were also seen to increase with increasing salinity, which is in contrast to the salinity-driven patterns in the composition of prokaryote communities , which proposes that environmental filtering has a weaker effect on smaller organisms (prokaryotes) than on larger organisms (m [NCM]) of the microeukaryotic communities being lower than those of the prokaryotic communities indicated that the dispersal ability of microeukaryotes was lower than that of prokaryotes. Why the slope of the microeukaryote distance-decay pattern was steeper than that for prokaryotes could be partially explained by this  and biotic factors  as the main driver of microbial communities. However, the neutral theory argues that stochastic processes, such as birth, death, migration, speciation, and limited dispersal, shape microbial community structure . The NCM36–rganisms . Moreove by this . A complCo-occurrence network analysis can reveal significant and strong correlations between microbial taxa . In thisAlthough the stability of microbial communities can be used to draw inferences about the functioning of that ecosystem , 53, wheUnlike prokaryotes, the network interactions of microeukaryotes were more stable in the continental shelf than in the Lena River estuary. The Lena River estuary experiences constant freshwater/oceanic water input and exchange, creating a large salinity gradient from the estuary to the continental shelf. Salinity changes affect not only the biodiversity of eukaryotes and the structure of eukaryotic communities but also the network stability . The larIn this study, the prediction of bacterial functional profiles focused on key genes related to methane metabolism because our sampling sites were in geographical proximity to the recently discovered field of methane seepage, which significantly contributes to the methane emissions in the Laptev Sea . The occTo sum up, our findings provided baseline data that improve our understanding of the spatial variation and potential functions in microbial communities from the Lena River estuary to the Laptev Sea. The spatial patterns and structures in prokaryotic and microeukaryotic communities varied significantly across the transect. The results of the NCM and DNCI indicated that stochastic processes have a dominant effect on the assembly of prokaryotic communities. However, the contribution of stochastic processes to the assembly of microeukaryotic communities was weak by comparison. Changes in salinity caused by terrestrial input might affect the stability and resilience of estuarine microbial interactions from the Lena River estuary to the Laptev Sea. Compared to the dominant taxa, low-abundance groups (“gatekeepers”) had a greater contribution to the topology and stability of networks. Prokaryotes exhibit stronger and more interactive networks in the Lena River estuary than on the continental shelf. Microeukaryotic networks were more stable in the continental shelf. The results of this study demonstrate that biotic interactions play a central role in maintaining the stability of microbial networks and the integrity of the Laptev Sea microbial ecosystem.in situ.A total of 20 stations from the Lena River estuary to the continental shelf in the Laptev Sea were visited, and surface water samples (2 L each from a depth of about 1 m) were collected during September and October 2018 . The samGTGCCAGCMGCCGCGGTAA-3′) and 907R (5′-CCGTCAATTCCTTTGAGTTT-3′) (CCAGCASCYGCGGTAATTCC-3′) and TAReukREV3R  (Q < 20 or length < 200 bp) were discarded using QIIME (version 1.17) (https://github.com/qian8341/SUPPLEMENTAL-MATERIAL]).DNA was extracted by the lysozyme-SDS-phenol-chloroform method . The V4-GTTT-3′) , and theTYRA-3′)  broad miTYRA-3′) . The conon 1.17) . Tags weon 1.17) , after con 1.17) , 63. EacThe sampling map, temperature, and salinity diagrams were generated using ODV software . The PRIP < 0.01; r > 0.8 or r < −0.8) (f) was calculated as the ratio of the number of disconnected subgraphs (CL) to the overall number of nodes (N) in each network, calculated as log (CL)/log(N)  based on Spearman’s rank correlations ( < −0.8) . OTUs of)/log(N) . The ave)/log(N) . Modular)/log(N) . Networkhttps://www.omicstudio.cn) for preprocessing.Profiling of predictive microbiota was analyzed by using PICRUSt2 . The bioPRJNA786341 and PRJNA786334.The sequence data generated in the present study have been deposited in the NCBI Sequence Read Archive (SRA) database under accession no.


Sarcoidosis is a multi-organ medical condition that is characterized by the formation of granulomas. We aimed to identify a correlation between each sarcoidosis blood biomarker and cystatin C (Cys-C) in sarcoidosis patients. We report a case of a 60-year-old man with sarcoidosis. The correlation between his Cys-C and each blood biomarker level and that between each blood biomarker and serum creatinine levels were determined using linear regression. Serum Cys-C correlated with each blood biomarker of sarcoidosis, while creatinine did not. These findings suggest that Cys-C is a potential blood biomarker for sarcoidosis. Sarcoidosis is a multi-organ disease characterized by granuloma formation in various locations of the body. The clinical manifestations at onset and the subsequent clinical course vary. Although sarcoidosis frequently affects the hilar mediastinal lymph nodes, lungs, eyes, and skin, it can also affect most organs, including the nerves, muscles, heart, kidneys, bones, and digestive organs. Angiotensin-converting enzyme (ACE), soluble interleukin (IL)-2 receptor (sIL-2R), and lysozyme are representative blood biomarkers for sarcoidosis.Although there are various markers for sarcoidosis, one blood biomarker alone is insufficient for establishing a sarcoidosis diagnosis. Hence, the identification of new blood biomarkers is warranted. Cystatin C (Cys-C) is a protease inhibitor that inhibits enzyme-induced cytoplasmic and tissue damage and bacterial and viral growth. Cys-C is used as a marker for early renal dysfunction. Recently, Cys-C has been reported to promote naive helper T-cells differentiation into inflammatory Th1/Th17 cells ,3.Here, we report a 60-year-old man with an unremarkable medical history who was diagnosed with sarcoidosis. We aimed to identify the correlation between the blood biomarkers of sarcoidosis and Cys-C in a patient with sarcoidosis.A 60-year-old Japanese man with serum creatinine (SCr) levels of 0.7 mg/dL was referred to our clinic for the management of renal dysfunction. The patient had no symptoms of arthritis, fatigue, weight loss, lymphadenopathy, or skin rash. He had an unremarkable urinalysis during a medical checkup in March 2021. However, a blood test performed in October 2021 showed a serum creatine (SCr) level of 1.65 mg/dL. An abdominal ultrasound revealed left hydronephrosis. Therefore, he visited the urologist at his local hospital, where he was diagnosed with a left ureteral stone. The stone was spontaneously resolved, and the hydronephrosis improved. No kidney stone analysis was performed. Although his hydronephrosis improved, he was referred to our department in January 2022 because of persistent renal dysfunction. The patient's medical history included hyperuricemia and hyperlipidemia. He regularly took ethyl omega-3 fatty acids, febuxostat, and atorvastatin. His dietary supplements also included sesamin and lactoferrin.Blood tests revealed renal dysfunction SCr: 1.34 mg/dL), a 2.6-fold discrepancy between estimated glomerular filtration rate (eGFR) and creat (43.1 mL/min/BSA and eGFR Cys-C (16.3); and hypercalcemia . Blood tests also revealed fractional excretion of calcium at 7.6%  and low levels of parathyroid hormone (PTH) level (intact PTH 2.8 pg/mL). In addition, no elevation of PTH-related protein-intact level was not elevated (1.1 pmol/L), and high vitamin D levels (1-25 OH-D 124 pg/mL) were detected  T2WI) showed the three stripe sign and dark star signs in a granuloma in the right thigh muscle layer Figure . GalliumWI showedThe eGFR Cys-C and eGFR creat were compared using the paired t-test, and correlations between Cys-C and sarcoidosis blood biomarkers  were assessed using linear regression analysis. The correlation between SCr and each blood biomarker of sarcoidosis was evaluated using the same method. Prism version 7.0  was used for statistical analysis. A p-value of <0.05 was considered statistically significant. eGFR Cys-C was significantly lower than eGFR creat . A correlation was observed between Cys-C and ACE, IL-2R, and lysozyme Figure .As a case report, the study was exempt from ethics approval. The patient provided informed consent.Although the cause of sarcoidosis is unknown, it is thought that, in susceptible individuals, there is a Th1-type cellular immune response  to the antigen that causes the disease, resulting in granuloma formation in various organs of the body ,3.ACE, sIL-2R, and lysozyme are included in the criteria for the clinical diagnosis of sarcoidosis in Japan . In patiCys-C is an indicator of renal function that is independent of muscle mass. Although Cys-C levels rarely fluctuate in conditions other than renal dysfunction, they can be elevated in patients with melanoma, rectal cancer, and corticosteroid use and decreased in those with HIV infection and cyclosporine use -12. In oThis is the first report on a correlation between the three biomarkers for sarcoidosis and Cys-C. The mechanism by which cystatin C is elevated in patients with sarcoidosis was not established in this report, necessitating further elucidation. We reported Cys-C as a potential blood biomarker for sarcoidosis. Since this is a report of a single patient, more reports should be accumulated and reviewed.