Drosophila fat body. Here we show that free-running oscillations of the fat body clock require clock function in the PDF-positive cells of the fly brain. Interestingly, rhythmic expression of the cytochrome P450 transcripts, sex-specific enzyme 1 (sxe1) and Cyp6a21, which cycle in the fat body independently of the local clock, depends upon clocks in neurons expressing neuropeptide F (NPF). NPF signaling itself is required to drive cycling of sxe1 and Cyp6a21 in the fat body, and its mammalian ortholog, Npy, functions similarly to regulate cycling of cytochrome P450 genes in the mouse liver. These data highlight the importance of neuronal clocks for peripheral rhythms, particularly in a specific detoxification pathway, and identify a novel and conserved role for NPF/Npy in circadian rhythms.Metabolic homeostasis requires coordination between circadian clocks in different tissues. Also, systemic signals appear to be required for some transcriptional rhythms in the mammalian liver and the DOI:http://dx.doi.org/10.7554/eLife.13552.001 Many processes in the body follow rhythms that repeat over 24 hours and are synchronized to the cycle of day and night. Our sleep pattern is a well-known example, but others include daily fluctuations in body temperature and the production of several hormones. Internal clocks located in the brain and other organs drive these rhythms by altering the activity of certain genes depending on the time of day.Animals have specific organs that contain enzymes needed to break down toxic molecules in the body, and the levels of several of these enzymes rise and fall over each 24-hour period. In mammals, these enzymes are found in the liver, but in insects they are found in an organ called the fat body. Here, Erion, King et al. set out to determine the extent to which the internal clock in the brain influences the daily rhythms of these enzymes.The experiments show that a hormone released by the nervous system is required for the levels of the detoxifying enzymes to change in 24-hour cycles. This hormone – termed Neuropeptide F in fruit flies and Neuropeptide Y in mice – is also known to stimulate both mice and fruit flies to eat. Since toxic molecules often enter the body during feeding, Erion, King et al. speculate that it may be beneficial to link the detoxification process to feeding by using the same mechanism to control both processes. The next step following on from this work would be to find out exactly how neuropeptide F drives the 24-hour rhythms in the fat body and other organs.DOI:http://dx.doi.org/10.7554/eLife.13552.002 Circadian clocks constitute an endogenous timekeeping system that synchronizes behavior and physiology to changes in the physical environment, such as day and night, imposed by the 24 hr rotation of the earth . A coherDrosophila melanogaster, the neuronal clock network is comprised of roughly 150 circadian neurons, which are grouped based on their anatomical location and function in the brain (ds) and the lateral posterior neurons (LPNs). The dorsal neurons are divided into three subgroups, dorsal neurons (DN) 1, 2, and 3. The small LNvs (sLNvs) have traditionally been referred to as the central clock because they are necessary and sufficient for rest:activity rhythms under constant conditions , albeit with compromised amplitude , requires PDF-expressing cells in constant darkness. Interestingly, however, clocks in the NPF-expressing subset of LNds, as well as NPF itself, are important for driving rhythmic expression of specific cytochrome P450 genes that cycle independently of the fat body clock. Lastly, we show that Npy, the mouse homolog of NPF, regulates transcriptional circadian output in the mouse liver. Microarray analyses reveal that Npy contributes to the rhythmic expression of hundreds of transcripts in the liver, including a subset of cytochrome P450 genes. In summary, we identified a conserved role for NPF/Npy neuropeptides in the circadian system in coupling neuronal clocks to transcriptional output in peripheral tissues in flies and mice.The relative simplicity of fly neuroanatomy and physiology, the vast array of genetic tools, and the conservation of molecular mechanisms with mammals make the fly an ideal organism to dissect complex interactions between physiological systems. In this study, we found that neural clocks regulate circadian gene expression in the fly fat body, a peripheral metabolic tissue. We demonstrate that cycling of the core clock gene, Drosophila are completely autonomous, e.g. malphigian tubules  and CYCLE (CYC), activate expression of the genes period (per) and timeless (tim) and PER and TIM proteins feedback to inhibit the activity of CLK-CYC (Pdf-GAL4/UAS-CLKΔ flies showed that a majority of the flies had arrhythmic locomotor activity in constant darkness (DD)  , confirmper in abdominal fat bodies over the course of the day  under a 12 hr light: 12 hr dark (LD) cycle . per levels were rhythmic in the fat body of control flies on DD2. In contrast, flies expressing CLKΔ in the LNvs showed an apparent lack of per rhythms in the fat body  cycle . Unlike t signal . Therefoay of DD , we testDrosophila have photoreceptors. Therefore, in addition to entraining clocks to the external environment, LD cycles can drive rhythmic transcription via clock-independent pathways , a lipase and CG17562, an oxidoreductase was eliminated in jrkClk mutants to produce an intermediate level of gene expression throughout the day (sex-specific enzyme 1 (sxe1), a cytochrome P450, and CG14934, a purported glucosidase involved in glycogen breakdown, were greatly reduced in jrkClk mutants , sNPF (short neuropeptide F), and ITP (ion transport peptide) in different cells, with some overlap  in addition to Npf-GAL4 and CLKΔ transgenes. Tub-GAL80ts ubiquitously expresses a temperature-sensitive GAL80 protein, which represses GAL4 activity at the permissive temperature of 18°C  to induce CLKΔ expression. We found that after shifting flies to 30°C, expression of sxe1 remained rhythmic and similar to 18°C controls, suggesting adult-specific clock ablation in NPF-positive neurons is either incomplete or insufficient to affect sxe1 rhythms  required for this assay. Importantly though, rhythmic expression of Cyp6a21 was dampened by adult-specific clock ablation in LNd neurons , as npf mutants are not available. Driving UAS-npf RNAi under Npf-GAL4 resulted in dampened but still rhythmic sxe1 expression  mutant mice and wild type controls over the course of an entire day. Although there is no direct mammalian homologue of sxe1, we noticed that a similar P450 enzyme involved in xenobiotic detoxification, Cyp2b10, also continues to cycle in animals lacking functional liver clocks , is required for P450 synthesis  and a stringent P-value cutoff of p<0.01 to detect cyclic transcripts, we found that 289 transcripts were cyclic in controls but not in Npy KO, indicating that the oscillation of these transcripts is under the regulation of Npy signaling . We cannot formally exclude the possibility that expression of CLKΔ or CYCΔ in non-clock cells or even in the gut . These results suggest there are additional mechanisms regulating circadian rhythms in the fat body/liver. Why would more than one mechanism exist to couple rhythmic gene expression in a specific peripheral tissue to other clocks? One possibility is that different mechanisms regulate distinct phases of circadian gene expression. Alternatively, different mechanisms may couple gene expression to different cell populations, processes, or behaviors.Since NPF promotes feeding in a larvae  and Npy a larvae . However animals , indicater genes . Thus, wer genes . SimilarCyp6a21, sxe1 and Cyp2b10, are associated with detoxification . The following flies were obtained from Bloomington Drosophila Stock Center: Npf-GAL4 (#25681), UAS-CLKΔ (#36318), UAS-CYCΔ (#36317), tub-GAL80ts (#7018), and npfr mutant (#10747).Flies were grown on standard cornmeal-molasses medium and maintained at 25°C. The following strains were used: Iso31 , Pdf-GAPdf-GAL4 , 911-GALPdf-GAL4 ), Dvpdf-df-GAL80 , Clkjrk   were used to monitor rest:activity rhythms under constant conditions. Roughly 1 week old male flies were entrained for at least 3 days to 12 hr light: 12 hr dark cycles (LD) and then transferred to constant darkness for at least 7 days. Data were analyzed using ClockLab software (Actimetrics) and rhythmicity of individual male flies was determined for days 2–7 of DD as described previously .tub-Gal80ts experiments, flies were raised at 18°C. Control flies were kept at 18°C, while the experimental flies were shifted to 30°C, the restrictive temperature for Gal80ts, for at least 4 days before collection.Male flies (roughly 4–7 days old) were entrained to a 12:12 LD cycle at 25°C for at least 3 days before they were harvested. The abdominal fat body was obtained by separating the fly abdomen from the rest of the body and then removing all internal organs, leaving the fat body attached to the cuticle to be collected on dry ice for RNA extraction. For Npy knockout mice were obtained from The Jackson Laboratory (004545) along with their background strain for controls (002448). Genotyping primers are listed on the Jackson website. 8–12 weeks old male mice were entrained to 12:12 LD cycles and fed a standard ad lib diet. Livers from Npy knockouts and their background controls were collected every 4 hr starting at lights on (ZT0) and immediately frozen in liquid nitrogen. 3–4 male mouse livers were collected at every timepoint for each genotype. All procedures were approved by the University of Pennsylvania Institutional Animal Care and Use Committee.αtubulin (Forward 5’ CGTCTGGACCACAAGTTCGA 3’ and reverse 5’ CCTCCATACCCTCACCAACGT 3’), per (Forward 5’ CGTCAATCCATGGTCCCG 3’ and reverse 5’ CCTGAAAGACGCGATGGTG 3’), Cyp4d21/sxe1 (Forward 5’ CTCCTTTGGTTTATCGCCGTT 3’ and reverse 5’ TTATCAGCGGCTTGTAGGTGC), sxe2 (Forward 5’ TGCGGTACGATCTTTATACGCC 3’ and reverse 5’ CTAACTGGCCATTTCGGATTGA 3’), CG14934 (Forward 5’ GGAAATCACGACAATCCTCGA 3’ and reverse 5’ CCCAACTCCTCGCCATTATAAG 3’), Cyp6a21 (Forward 5’ GTTGTATCGGAAACCCTTCGATT 3’ and reverse 5’ AACCTCATAGTCCTCCAGGCATT 3’), and CG117562 (Forward 5’ACCACAGAGGTGAAACGCATCT 3’ and reverse 5’CAGCAGCAGTTCAAATACCGC 3’). Transcript levels were normalized to those of αtubulin to control for the total RNA content in each sample.For each time point, fat bodies from 12 male flies were collected for RNA preparation. Total RNA was extracted using Trizol reagent  and purified using RNeasy Mini Kit  according to manufacturer’s protocol. All RNA samples were treated with RNase-free DNase (Qiagen Inc.). RNA was reverse transcribed to generate cDNA using a High Capacity cDNA Reverse Transcription kit . Quantitative RT-PCR was performed on a 7900HT Fast-Real-Time PCR (Applied Biosystems) using SYBR Green (Life Technologies). The following primer sequences were used for qPCR: Cyp2b10 (Forward 5’ GACTTTGGGATGGGAAAGAG 3’ and reverse 5’ CCAAACACAATGGAGCAGAT 3’), 36B4 (Forward 5’ TCCAGGCTTTGGGCATCA 3’ and reverse 5’ CTTTATCAGCTGCACATCACTCAGA 3’), Rev-erb alpha (Forward 5’ GTCTCTCCGTTGGCATGTCT 3’ and reverse 5’ CCAAGTTCATGGCGCTCT 3’) and Alas1 (PrimerBank ID 23956102a1) (36B4.Kits and procedures to isolate RNA and generate cDNA from mouse livers are the same as described above for fly fat bodies. The following primer sequences were used for qPCR: 56102a1) . TranscrP values of less than 0.05 were considered significant. We also used two-way ANOVA for repeated measures and a Tukey’s post hoc test for differences across time (GraphPad Prism). P-values are reported in Significant circadian rhythmicity of transcript levels was determined using the JTK_Cycle algorithm . P valueNpy KO and wild type mice were collected every 4h over 24h (n = 2 per genotype and timepoint). RNA was purified as described above. Expression profiling was done at the Penn Molecular Profiling Facility using Mouse Gene 2.0 ST Arrays . For extracting expression values of transcripts, raw CEL files were analyzed with the RMA algorithm (https://github.com/gangwug/MetaCycleV100.git) was used to detect circadian transcripts from time-series expression data in the wild type (WT) and Npy knockout (KO) groups, respectively. Key parameters in MetaCycle were the periodicity detection algorithms, JTK_CYCLE  regulates P450 genes in the mouse liver, suggesting that this mechanism is evolutionary conserved. Although the manuscript provides new and interesting data, we have a concerns regarding the interpretation and significance of the data. These are given below. If these important concerns are addressed, this will be a strong addition to the literature. The inclusion of fly and mouse data makes a strong statement and the interesting relationship between circadian timing, metabolism and behaviour becomes more intriguing as it is detailed with genes like npy and sxe1.In this manuscript, Erion et al. study an important issue in circadian biology: how peripheral circadian oscillators communicate with the main pacemaker in Essential revisions:sxe1 rhythms. Unless we are missing something, does this not contradict the main model stating a relationship between NPF and sxe in adults? A clear explanation for this should be provided.sxe1 and Cyp6a21 mRNA level and oscillations are direct through NPF or mediated by another oscillating behavior such as feeding or mating. Determining whether time-restricted feeding (TRF) can have an effect in the phase and amplitude of sxe1 and Cyp6a21 rhythms in the NPF-DClock flies will help determine whether the observed effects could be due to secondary effects related to feeding or not. Even understanding how TRF affect sxe1 and Cyp6a21 oscillations could be illuminating in this aspect and provide support for the main hypothesis on the manuscript. Further, it is indeed surprising that the authors don't use the data in Gill et al.  to determine if this is the case.Another important issue is whether the effect of the brain clock over sxe1 and Cyp6a21 are abolished on JrkClk mutants and that Npf itself it is regulated by CLK. However, these flies have multiple defects and determining the same parameters in tim or per mutants would be more informative. The authors should either speedily include these results or explain why they are not necessary.The authors clearly demonstrate that the oscillations of eLife. Your full submission has been evaluated by a Senior editor and three peer reviewers, one of whom was also guest Reviewing editor, and the decision was reached after discussions between the reviewers. Based on our discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.Thank you for choosing to send your work entitled "Brain clocks and NPF regulate circadian gene expression in a peripheral metabolic tissue" for consideration at Drosophila. Previously, the Sehgal lab has shown that 40% of cycling fat body transcripts oscillate independently from the local fat body clock, i.e., they are driven by factors external to the fat body clock. The situation is vaguely similar to mammalian liver. The claim now is that the neuropeptide NPF is produced from some clock neurons and regulates the fat body clock. However, all three reviewers had only muted enthusiasm for the current version of this paper. They were also unanimous in wanting a lot more data to support the claim and to raise the profile of the paper. It seemed unlikely to all the reviewers that the authors would be able to gather these data in the requisite 2-month time interval recommended by eLife. Moreover, the authors run a substantial risk that the data will change major conclusions of the paper. So with these issues in mind, the reviewers agreed that a rejection decision is more appropriate than revise and that the authors return to eLife with a new manuscript if that is the route they choose; this will obviate any temporal issues.This is an interesting paper from Sehgal and colleagues, which concerns the control of circadian cycling of fat body gene expression by the brain clock in Reviewer #1:Drosophila. The story is that the neuropeptide NPF is produced from some clock neurons, and it regulates the fat body clock. However, the focus is on a single fat body transcript (out of the 40% that have been reported to oscillate independently from the fat body clock), and it is unclear that this result is applicable to a wider set of mRNAs. The data also make me concerned about developmental effects. Moreover, I am not convinced that any aspect of the fat body clock is influenced by NPF – as distinct from some other effect on gene expression of a rather limited subset of fat body mRNAs. The authors also admit that the effect of NPF on the fat body is almost certainly indirect (through feeding?), so we do not know anything mechanistic about how NPF is exercising its influence.This paper from Sehgal and colleagues concerns the control of the fat body clock by the brain clock in I hope the specific comments will be of help to the authors.1) In the subsection “The Central Clock Regulates the Fat Body Clock in Constant Darkness”: It would have been much better to use another tool rather than CLK∆, a tool that isn't so disruptive of gene expression. Also, the authors should definitely use gene switch (GS) to avoid developmental effects.2) 3) The three scenarios the authors describe in the subsection “Rhythmic Expression of Fat Body Transcripts that Cycle Independently of the Local Tissue Clock Require Organismal Circadian Function” include a straw man. The effect of nutrients on the fat body clock depends on the robustness of feeding rhythms, which are rather weak in flies.jrkClk effect could be developmental. This could be addressed (imperfectly but nonetheless appropriately) with elav GS.4) The sxe1, the gene that they focus on for the rest of the paper just isn't expressed in jrkClk. So how does this relate to circadian oscillations?5) The authors describe the level of 2 of the 4 fat body cycling genes  as "attenuated." This is inaccurate in my view, as mRNA levels are virtually absent. And it is quite troubling that 6) The authors spend a large amount of text describing negative results, the different clock neuron drivers that have no effect on rhythmicity. It is strange that they do not also use the more typical Clk 4.1 DN1 driver.Npf-GAL4 driver. However, disrupting the clock in these neurons doesn't really eliminate rhythmic expression of sxe1 mRNA as much as it dramatically lowers expression, i.e., I can imagine a rhythm with very low levels of mRNA.7) The positive result is with the tub-GAL80ts to reduce the influence of these effects. Second there is no attempt to address clock neuron specificity. (Could there be relevant CLK and CYC expression in non-clock neurons? Would the addition of cry-GAL80 be informative?)8) There are several other difficulties with this result. First there is the usual developmental effects possibility, i.e., no use of gene switch or sxe1 expression levels, not necessarily rhythmicity) whereas the knockdown is much less potent.9) The data in cyp2b10 mRNA but still some cycling to my eye. Moreover, this is a systemic KO and all through development.10) Unfortunately, the mammalian result suffers from similar criticisms. There are lower levels of Reviewer #2:Drosophila. While I find the work solid and important for the field, I have a few concerns about the interpretation and significance of the data, especially in light of a recent manuscript from the Panda lab . The main claim in the manuscript is that sxe1 and sxe2, two genes that cycle independently of the fat body clock require clocks in other tissues. This seems more prominent for sxe1, which they report is regulated by NPF-expressing neurons.The manuscript by Erion et al., addresses an important issue in circadian biology: the communication between central and peripheral circadian oscillators in sxe1 mRNA level and oscillations is direct through NPF or mediated by another oscillating behavior like feeding or mating. I think that determining whether time-restricted feeding (TRF) can have an effect in the phase and amplitude of sxe1 rhythms in the NPF-DClock flies will help determine whether the observed effects could be due to secondary effects related to feeding or not. Even understanding how TRF affect sxe1 oscillations could be illuminating in this aspect. I looked into the Gill et al. manuscript, but unhappily sxe1 was not found among the oscillating mRNAs neither in heads or periphery. This is likely due to difference in strains.The main issue is whether the effect of the brain clock over In the same line as above, some of the Npf-related effects might be developmental and experiments using induced expression of DClk in the NPF cells (i.e. by the use of the GAL80ts system) could rule out this possibility.sxe1 oscillations. However, I would be surprised if this will be the only gene regulated in this way and the manuscript will gain a lot from the description of a second gene in this route.Despite my concern stated above, the authors find good support for their hypothesis that NPF neurons regulate Based on the experiment performed in Reviewer #3:Drosophila fat body continue to oscillate in the absence of a functional circadian oscillator in the fat-body. Although food, temperature cycle, and light can drive oscillations in the absence of a local peripheral clock, the authors focused on specific transcripts that cannot be driven by these stimuli and hence were dependent on clocks elsewhere. They have systematically addressed this issue by examining the fat-body oscillation of sxe genes in fly strains lacking circadian clock in specific neurons and have found the NPF expressing neurons and the NPF signaling system somehow drives peripheral oscillations. To test the relevance of this regulation in mammals, they have tested Cyp10b2 gene expression in the liver of NPY knockout mice and have shown dampened oscillation. The authors imply that this is the first time a brain derived neuropeptide drives circadian oscillation in a peripheral organ and suggest NPY may be targeted to improve circadian regulation of xenobiotic metabolism.This study is a follow-up to the author's earlier manuscript showing that ~40% of oscillating transcripts in the Most of the experiments are straightforward in which the authors test mRNA expression of candidate gene by qPCR in various fly lines or liver gene expression in NPY knockout mice.Given the role of NPY and NPF in feeding, and feeding is known to drive ~24 hr oscillation in gene expression in the complete absence of a clock anywhere in the body, it is important to have some measure of feeding pattern in these experiments in both flies and mice.In rodents, NPY is also expressed in peripheral tissues including the gut. A recent paper from Chang's lab has shown the gut plays a profound role in driving liver circadian gene expression. Therefore, it is premature to state the CNS expression of NPY is driving liver circadian gene expression. The authors may use a hypothalamus specific knockout of NPY if they want to drive this message. Along the same line, it will be worth examining whether NPF system is expressed in the peripheral organs of flies.With the lab's track record in genome wide studies and this paper being a follow-up of their earlier gene expression studies in fat body, it is worth including a RNA-seq or microarray study to examine to what extent the NPF system in flies and NPY system in mice regulate circadian fatbody/liver gene expression independent of feeding pattern. Such experiments will expand the impact and relevance of the paper beyond just a couple of liver transcripts. Essential revisions:sxe1 rhythms. However, it does affect cycling of the other cytochrome P450 gene we studied, cyp6a21 ( cyp6a21 . These fAnother important issue is whether the effect of the brain clock over sxe1 and Cyp6a21 mRNA level and oscillations are direct through NPF or mediated by another oscillating behavior such as feeding or mating. Determining whether time-restricted feeding (TRF) can have an effect in the phase and amplitude of sxe1 and Cyp6a21 rhythms in the NPF-DClock flies will help determine whether the observed effects could be due to secondary effects related to feeding or not. Even understanding how TRF affect sxe1 and Cyp6a21 oscillations could be illuminating in this aspect and provide support for the main hypothesis on the manuscript. Further, it is indeed surprising that the authors don't use the data in Gill et al.  to determine if this is the case.sxe1, it is not the only, and most likely not even the major, contributor to rhythms of genes driven by NPF/NPY:We include a time-restricted feeding (TRF) experiment , and lissxe1 as well as of the period (per) clock gene. We found that restricting food to a time of day when flies do not normally feed alters the phase of both per and sxe1 cycling  shifts sxe1 expression even though sxe1 cycling does not require the fat body clock.We conducted time-restricted feeding (TRF) experiments and assayed effects on cycling of  cycling . Thus, ssxe1 are developmental (as noted above), so NPF could not be driving rhythms of sxe1 through cycles of feeding.We have found that effects of NPF on Cyp6a21, we showed previously that while it can be driven by restricted feeding (RF) in JrkClk flies the rhythm is not as robust as seen upon RF in wild type flies . Thus, it does not appear to be driven by a feeding cycle alone. This is consistent with the fact, also pointed out by previous Reviewer 1, that feeding cycles are quite low in amplitude in flies and unlikely to account for robust molecular rhythms.Regarding sxe1 cycling is not regulated by feeding.We checked the data of Gill et al. as suggested by the reviewer. They show that http://circadian.salk.edu/about.html) does not indicate restoration of Cyp2b10 cycling by restricted feeding of clockless mice.The Panda database  and cyp6a21 mRNAs cycle in wild type but not in three different clock mutants – 0, tim01perand jrkClk. Claridge-Chang et al. did not detect cycling of Npf mRNA and neither did we , which showed that r did we . While NSee the webpages on each gene for data and graphs:http://biorhythm.rockefeller.edu/probeset.php?probeset=152958_atsxe1 (cyp4d21): http://biorhythm.rockefeller.edu/probeset.php?probeset=142190_atcyp6a21: http://biorhythm.rockefeller.edu/probeset.php?probeset=144175_atnpf: [Editors’ note: the author responses to the previous round of peer review follow.]Reviewer #1: This paper from Sehgal and colleagues concerns the control of the fat body clock by the brain clock in Drosophila. The story is that the neuropeptide NPF is produced from some clock neurons, and it regulates the fat body clock. However, the focus is on a single fat body transcript (out of the 40% that have been reported to oscillate independently from the fat body clock), and it is unclear that this result is applicable to a wider set of mRNAs. The data also make me concerned about developmental effects. Moreover, I am not convinced that any aspect of the fat body clock is influenced by NPF – as distinct from some other effect on gene expression of a rather limited subset of fat body mRNAs. The authors also admit that the effect of NPF on the fat body is almost certainly indirect (through feeding?), so we do not know anything mechanistic about how NPF is exercising its influence. We agree with the reviewer that NPF does not influence the clock in the fat body, but rather a “limited subset of fat body mRNAs”. Indeed, we make the case in the manuscript that the clock is notaffected, as per cycling is normal when the clock is disrupted in NPF cells. ~30 transcripts in the fat body cycle independently of the fat body clock, and appear to be controlled by different mechanisms. For instance, in other work we have found that cycling at least one of these transcripts requires insulin-producing cells. The transcripts reported in this manuscript, sxe1 and a second characterized in the revised manuscript, cyp6a21, depend on NPF. As noted below, we have now conducted experiments to address the developmental issue raised by the reviewer. The identification of NPF as a neural factor that drives rhythmic expression in a metabolic tissue provides a mechanism for a longstanding question of interest in the circadian field, namely how coordinated action of clocks in different tissues regulates circadian outputs (metabolic gene expression in this case).Exactly how NPF does so is, we believe, beyond the scope of this manuscript. As discussed in detail in response to Reviewer 3 , feeding may contribute to the effects of NPF/NPY, but it is unlikely to be the only mechanism.1) In the subsection “The Central Clock Regulates the Fat Body Clock in Constant Darkness“: It would have been much better to use another tool rather than CLK∆, a tool that isn't so disruptive of gene expression. Also, the authors should definitely use gene switch (GS) to avoid developmental effects.elav GS) constituted too blunt a tool, we used the longer and more laborious method of coupling Npf-GAL4 with temperature sensitive tubulin-GAL80. Restricting clock disruption in NPF cells to the adult stage did not affect sxe1 cycling, suggesting that the effect is developmental. However, it did affect cycling of cyp6a21. We conclude that NPF has differential effects on rhythmically expressed transcripts in the fat body.We also shown data with dominant negative CYC. Unfortunately, no other tool effectively disrupts the clock. In response to the reviewer’s question, we conducted experiments to avoid developmental effects. As a gene switch (GS) driver was not available for NPF cells and we felt that disrupting the clock in all neurons (with 2) We agree and, therefore, have toned down the conclusions of this experiment.3) The three scenarios the authors describe in the subsection “Rhythmic Expression of Fat Body Transcripts that Cycle Independently of the Local Tissue Clock Require Organismal Circadian Function “include a straw man. The effect of nutrients on the fat body clock depends on the robustness of feeding rhythms, which are rather weak in flies.True. We have eliminated specific consideration of this scenario, which, in any case, turned out to be irrelevant.jrk effect could be developmental. This could be addressed (imperfectly but nonetheless appropriately) with elav GS.4) The Clkelav GS.As noted above, we have addressed the developmental issue with a better tool than jrk. So how does this relate to circadian oscillations?5) The authors describe the level of 2 of the 4 fat body cycling genes  as "attenuated." This is inaccurate in my view, as mRNA levels are virtually absent. And it is quite troubling that sxe1, the gene that they focus on for the rest of the paper just isn't expressed in Clkcyp6a21, which is expressed at reasonable levels when the clock in NPF cells is disrupted, but fails to cycle. Together with the new mouse data, which show effects of NPY loss on the circadian expression of many transcripts, without a significant change in levels of these transcripts, we believe we now present a strong case for effects of NPF/NPY on circadian oscillations.We have changed “attenuated” to “greatly reduced”. As noted, we have added analysis of a second transcript, 6) The authors spend a large amount of text describing negative results, the different clock neuron drivers that have no effect on rhythmicity. It is strange that they do not also use the more typical Clk 4.1 DN1 driver.We did try the Clk 4.1 driver, but failed to get consistent results. In our experience, the Clk 4.1 DN1 is a weak driver and so we switched to the stronger 911 driver. However, as noted in the text, even this driver did not indicate a role for DN1s.sxe1 mRNA as much as it dramatically lowers expression, i.e., I can imagine a rhythm with very low levels of mRNA.7) The positive result is with the NPF-gal4 driver. However, disrupting the clock in these neurons doesn't really eliminate rhythmic expression of sxe1 so an effect on levels alone cannot be excluded (note that jrkClkmutants show similar levels of sxe1 so this most likely reflects the effect of loss of the clock on sxe1). However, the revised manuscript shows that disrupting the clock in NPF cells clearly dampens cycling of cyp6a21.As noted above, manipulations of NPF cells do greatly reduce levels of ts to reduce the influence of these effects. Second there is no attempt to address clock neuron specificity. (Could there be relevant CLK and CYC expression in non-clock neurons? Would the addition of cry-GAL80 be informative?)8) There are several other difficulties with this result. First there is the usual developmental effects possibility, i.e., no use of gene switch or tub-GAL80tub-GAL80 (see above). Also, the manuscript demonstrates a role for the LNd clock neurons through the useof the DvPdf driver.We have addressed a possible developmental effect through the use of 9) The data in sxe1 is indeed developmental. However, adult-specific manipulation of NPF cells clearly has an effect on cycling of cyp6a21.As our new data indicate, the effect of NPF on 10) Unfortunately, the mammalian result suffers from similar criticisms. There are lower levels of cyp2b10 mRNA but still some cycling to my eye. Moreover, this is a systemic KO and all through development. In response to Reviewer 3’s last comment, we conducted microarray analysis to compare cycling gene expression in wild type and NPY knockout livers, and find that many liver transcripts require NPY for rhythmicity. We show also that average levels of transcripts are not altered in NPY livers.Reviewer #2:The manuscript by Erion et al., addresses an important issue in circadian biology: the communication between central and peripheral circadian oscillators in Drosophila. While I find the work solid and important for the field, I have a few concerns about the interpretation and significance of the data, especially in light of a recent manuscript from the Panda lab . The main claim in the manuscript is that sxe1 and sxe2, two genes that cycle independently of the fat body clock require clocks in other tissues. This seems more prominent for sxe1, which they report is regulated by NPF-expressing neurons.The main issue is whether the effect of the brain clock over sxe1 mRNA level and oscillations is direct through NPF or mediated by another oscillating behavior like feeding or mating. I think that determining whether time-restricted feeding (TRF) can have an effect in the phase and amplitude of sxe1 rhythms in the NPF-DClock flies will help determine whether the observed effects could be due to secondary effects related to feeding or not. Even understanding how TRF affect sxe1 oscillations could be illuminating in this aspect. I looked into the Gill et al. manuscript, but unhappily sxe1 was not found among the oscillating mRNAs neither in heads or periphery. This is likely due to difference in strains.sxe1 as well as of the period (per) clock gene. We found that restricting food to a time of day when flies do not normally feed alters the phase of both per and sxe1 cycling As suggested by the reviewer, we conducted time-restricted feeding (TRF) experiments and assayed effects on cycling of  cycling . Thus, ssxe1 (B)and per (C)at different times of day in the fat body of flies maintained on ad lib food (blue) or restricted to food between ZT9 and ZT15 (red). Expression values are normalized to α-tubulin. Each data point represents the average of n=2-3 independent samples, and the error bars represent SEM.(A) The restricted feeding procedure for flies. Food was restricted to ZT9-ZT15, the trough of feeding determined in Xu et al. 2008. Flies were transferred to a 1% agar vial from a normal food vial on day 1. Restricted feeding was started on day 2 and continued until day 7 at which point the flies were collected.  Quantitative PCR was performed to assay expression of In the same line as above, some of the Npf-related effects might be developmental and experiments using induced expression of DClk in the NPF cells (i.e. by the use of the GAL80ts system) could rule out this possibility.sxe1 are developmental. However, a new gene we have characterized in the revised manuscript, cyp6a21, is regulated by NPF in adults.Upon the reviewer’s recommendation, we used the GAL80ts system to restrict expression of DClk to adult NPF cells. We show in the revised manuscript that effects of NPF on Despite my concern stated above, the authors find good support for their hypothesis that NPF neurons regulate sxe1 oscillations. However, I would be surprised if this will be the only gene regulated in this way and the manuscript will gain a lot from the description of a second gene in this route.cyp6a21.As suggested, we have included analysis of a second gene, Clk mutants .Based on the experiment performed in Reviewer #3:This study is a follow-up to the author's earlier manuscript showing that ~40% of oscillating transcripts in the Drosophila fat body continue to oscillate in the absence of a functional circadian oscillator in the fat-body. Although food, temperature cycle, and light can drive oscillations in the absence of a local peripheral clock, the authors focused on specific transcripts that cannot be driven by these stimuli and hence were dependent on clocks elsewhere. They have systematically addressed this issue by examining the fat-body oscillation of sxe genes in fly strains lacking circadian clock in specific neurons and have found the NPF expressing neurons and the NPF signaling system somehow drives peripheral oscillations. To test the relevance of this regulation in mammals, they have tested Cyp10b2 gene expression in the liver of NPY knockout mice and have shown dampened oscillation. The authors imply that this is the first time a brain derived neuropeptide drives circadian oscillation in a peripheral organ and suggest NPY may be targeted to improve circadian regulation of xenobiotic metabolism.Most of the experiments are straightforward in which the authors test mRNA expression of candidate gene by qPCR in various fly lines or liver gene expression in NPY knockout mice.Given the role of NPY and NPF in feeding, and feeding is known to drive ~24 hr oscillation in gene expression in the complete absence of a clock anywhere in the body, it is important to have some measure of feeding pattern in these experiments in both flies and mice.sxe1 to restricted feeding and found that it is not affected any more than per  in jrkClkflies the rhythm is not as robust as seen upon RF in wild type flies . Thus, it does not appear to be driven by a feeding cycle alone. Finally, the Panda database  does not indicate restoration of Cyp2b10 cycling by restricted feeding of clockless mice. While all these data do not exclude a role of feeding, they indicate that NPF/NPY likely does not act through feeding alone to drive expression of these peripheral genes. We note too that only a limited subset of circadian genes that are independent of the liver clock depend on NPY. If NPY were driving gene expression through rhythmic feeding alone, it would impact most liver genes. The potential role of feeding is now fully discussed in the Discussion section.As noted by Reviewer 1, feeding rhythms are relatively low amplitude and so it would be difficult to see changes in these. We did examine the response of In rodents, NPY is also expressed in peripheral tissues including the gut. A recent paper from Chang's lab has shown the gut plays a profound role in driving liver circadian gene expression. Therefore, it is premature to state the CNS expression of NPY is driving liver circadian gene expression. The authors may use a hypothalamus specific knockout of NPY if they want to drive this message. Along the same line, it will be worth examining whether NPF system is expressed in the peripheral organs of flies. We acknowledge that the mouse data do not exclude a role for NPY function in the periphery. This possibility is now mentioned. However, in the fly, our data with the DvPdf driver show that LNds are important for cycling of sxe1. In addition, the previous work of Jae Park and colleagues (2006) showed that in jrkClkmutants npf is downregulated in LNds, but not in other NPF-positive cells. While LNds and NPF could act independently to regulate sxe1, it is more likely that LNds are the source of NPF in this case.With the lab's track record in genome wide studies and this paper being a follow-up of their earlier gene expression studies in fat body, it is worth including a RNA-seq or microarray study to examine to what extent the NPF system in flies and NPY system in mice regulate circadian fatbody/liver gene expression independent of feeding pattern. Such experiments will expand the impact and relevance of the paper beyond just a couple of liver transcripts.We have done as the reviewer suggested, and examined the genome-wide impact of loss of NPY. The revised manuscript presents our new microarray data showing that the cycling of several liver transcripts is regulated by NPY.


We applied surface-enhanced Raman spectroscopy (SERS) to cationic gold-labeled endothelial cells to derive SERS-enhanced spectra of the bimolecular makeup of the plasma membrane. A two-step protocol with cationic charged gold nanoparticles followed by silver-intensification to generate silver nanoparticles on the cell surface was employed. This protocol of post-labelling silver-intensification facilitates the collection of SERS-enhanced spectra from the cell membrane without contribution from conjugated antibodies or other molecules. This approach generated a 100-fold SERS-enhancement of the spectral signal. The SERS spectra exhibited many vibrational peaks that can be assigned to components of the cell membrane. We were able to carry out spectral mapping using some of the enhanced wavenumbers. Significantly, the spectral maps suggest the distribution of some membrane components are was not evenly distributed over the cells plasma membrane. These results provide some possible evidence for the existence of lipid rafts in the plasma membrane and show that SERS has great potential for the study and characterization of cell surfaces. Colloidal gold particles, 5–20 nm in diameter, have been used for several decades in electron microscopy 8 to 1012. Although this level of SERS-enhancement is not seen on biological samples, enhancement by one or two orders of magnitude is possible. The level of SERS-enhancement obtained is known to be influenced by several factors. These include the shape of the nanoparticles More recently, metallic nanoparticles have found new applications in the field of biomedicine i.e., the molecular composition of the cell surface. This is information that is difficult to obtain in any other way.Most of the research to date with SERS uses nanoparticles which have been attached to reporter molecules. In these instances, the metallic nanoparticle has a small molecule attached to it; the signal from this molecule is greatly enhanced by the SERS effect and therefore can be detected at a much lower concentration than would otherwise be possible. Thus it is possible to detect the presence of reporter molecules at much lower levels than would be possible without the nanoparticle. This approach has been recently used to directly monitor real time events such as apoptosis in live cells Previous work has used gold-conjugated antibodies to attach the gold nanoparticles to the cell surface. However, there is not always a suitable cell surface antigen present, and there is also some debate as to the contribution of the antibody to the SERS-enhanced spectra. It is possible to attach nanoparticles to the cell surface using charge rather than an antibody/antigen interaction. Cationic gold will bind spontaneously to the negatively-charged glycocalyx This paper describes how cationic gold, followed by silver-intensification, can be used to enhance Raman spectra derived from the cell surface and thus obtain biomolecular information regarding the cell membrane. A schematic diagram of the protocol is shown in Fresh bovine corneas were obtained from a local abattoir  within 2 hours of death and transported to the laboratory on ice. They were immediately dissected out and fixed using a 4% glutaraldehyde in phosphate-buffered saline (PBS) solution.Cationic colloidal gold (15 nm) and identical silver-intensification kits were obtained from TAAB Laboratories  or from BBI International . The colloidal cationic gold was used as a stock solution and for this experiment was diluted using PBS to 1/10 and 1/100 concentrations.The corneal samples were initially washed in PBS employing 3 washes of 5 minutes each. They were then incubated with one of the cationic colloidal gold dilutions at room temperature for 1 hour. The control samples were incubated with PBS instead of the cationic colloidal gold. Subsequently, all samples were washed in distilled water for 3 washes of 5 minutes each in order to remove any excess unbound substrate.The silver-intensification was carried out at room temperature. The silver enhancement was performed for 20 minutes during which the samples were kept away from bright illumination in order to limit the effect it would have on the enhancement process. The cationic gold treated samples which were not silver-intensified were incubated in PBS for 20 minutes.Next, all the samples were washed with distilled water in 5 washes of 5 minutes each to ensure any unbound silver was washed off the samples. In preparation for analysis, the samples were dehydrated through an alcohol series by placing them in 50% ethanol for 5 minutes, followed by 70% ethanol for 10 minutes, 80% ethanol for 10 minutes, 90% ethanol for 10 minutes and, finally, washed 3 times in 100% ethanol for 10 minutes each before being air dried.−1 with a silicon calibration sample before analysing the samples. After calibration, the prepared corneal samples were placed upon MirrIR Low-E glass slides  before being examined in order to allow an increase in the Raman signal gathered. All the Raman spectra were measured at 50x magnification down the microscope within the 500–2000 cm−1 spectral range using 1% laser power (0.1 mW), a 10 second accumulation time and 4 accumulations over the target illumination area of 1 µm2. An 830 l/mm grating was also used for each spectrum taken. For each different class, 40 spectra were taken with 20 taken from each sample in each class and the data was compiled in WiRE software (Renishaw Inc.). Some peaks were artefacts caused by ambient lighting; these wavenumber peaks were excised from the spectra.Raman spectroscopy was carried out using a Renishaw InVia Raman machine with a 785 nm excitation laser , which was initially calibrated to 520.5 cmSeparate bovine cornea samples were prepared as described above and were finally dehydrated using HMDS. After the samples were dry, they were affixed to aluminium stubs and sputter-coated with gold before being analysed with a JEOL 5600 scanning electron microscope .The spectral data that was gathered was processed initially in OPUS software , whereupon the spectra underwent rubberband baseline correction and vector normalization. After completion, the processed data underwent multivariate computational analysis using MATLAB software  using the IRootLab MATLAB toolbox 2. The spectral range of the Raman map was 500–2000 cm−1, using a static grating; in total, 960 spectral points were collected over an 11 hour period. Wire software (Renishaw Inc.) was then used to generate false color images of the spectral map at discrete wavenumber peaks, using different colors to represent different intensities of Raman signal.Raman map images were taken of individual endothelial cells using a 50x objective (NA = 0.75) and a 100 nm precision motorized stage (Renishaw Inc.). Spectral points were taken in a raster pattern and were taken with 1 accumulation for each point at 0.1% laser power and 10 seconds acquisition time over a target area of 0.8×0.8 µmThe SEM results are shown in In PCA was employed to see how the enhanced spectra clustered compared to the control . Unsurprvia a vector taken from a central point, in this case it is the PBS control group. The groups with 1/10 silver-intensified gold and 1/100 silver-intensified gold were the only groups to show significant peaks whilst the other groups had peaks that were not above the set threshold value of significance. Interestingly, the plot from the 1/100 silver-intensified gold is very similar to the 1/10 silver-intensified gold over the 1000–600 cm−1 spectral region, although the degree of SERS-enhancement is much less. Following on from this, a SERS-enhanced spectral map at 611 cm−1 is shown . Cholesterol is a major component of animal cell membranes varying between often making up more than 25% of the membrane composition. Interestingly, the spectral map of this wavenumber shows there is high signal intensity in the center of the cell. One possible, speculative, explanation for this is that we are seeing the presence of lipid rafts. Lipid rafts are microdomains of the plasma membrane which have a different composition to other regions of the plasma membrane As part of this project we chose the region of the biomolecular spectra which was most enhanced. This peak centers on the wavenumber 611 cmIn conclusion, this work has shown that it is possible to get a 100-fold signal enhancement in cell surface spectra with silver-intensified cationic gold. The cationic gold attaches evenly to the cell glycocalyx and this methodology is useful for the study of the plasma membrane. The results have provided some possible evidence for the heterogeneous distribution of membrane components on corneal endothelial cells. This method does not require the use of antibodies, antibodies have both benefits and disadvantages; they allow a more targeted approach bringing the nanoparticles to a specific epitope on the cells surface but the procedure is more complex and time-consuming and there have been some concerns expressed as to what the contribution of the antibody might be to the spectra. Our study demonstrates an approach that has produced a 100-fold SERS-enhancement of the spectral signal which appears to be derived chiefly or solely from the endothelial cell membrane. Additionally, spectral mapping has provided possible evidence on the heterogeneity of some of the plasma membrane components.


IGHMBP2 gene, encoding the immunoglobulin μ-binding protein 2, leading to motor neuron degeneration. It is a rare and fatal disease with an early onset in infancy in the majority of the cases. The main clinical features are muscular atrophy and diaphragmatic palsy, which requires prompt and permanent supportive ventilation. The human disease is recapitulated in the neuromuscular degeneration (nmd) mouse. No effective treatment is available yet, but novel therapeutical approaches tested on the nmd mouse, such as the use of neurotrophic factors and stem cell therapy, have shown positive effects. Gene therapy demonstrated effectiveness in SMA, being now at the stage of clinical trial in patients and therefore representing a possible treatment for SMARD1 as well. The significant advancement in understanding of both SMARD1 clinical spectrum and molecular mechanisms makes ground for a rapid translation of pre-clinical therapeutic strategies in humans.Spinal muscular atrophy with respiratory distress (SMARD1) is an autosomal recessive neuromuscular disease caused by mutations in the  Spinal muscular atrophy with respiratory distress type 1 (SMARD1) is a form of SMA with respiratory distress because of diaphragma-tic involvement et al., in 1974 et al., in 1989 It was first described by Mellins SMARD1 seems to be similar to SMA, but it actually differs from a clinical and genetical point of view Its exact prevalence is not known yet, but studies have shown that diaphragmatic palsy affects approximately 1% of patients diagnosed with early onset SMA IGHMBP2) gene located on chromosome 11q13.2-q13.4 SMARD1 is caused by homozygous or compound heterozygous mutations in the immunoglobulin μ-binding protein 2 . SMARD1 is considered a fatal form of infantile motor neuron disease.The result of mutations in Life expectancy is very low, with patients dying within 13 months and only few surviving longer Respiratory distress is the main symptom of SMARD1 and it is characterized by being extremely severe and rapidly progressive The cause of respiratory distress is a diaphragmatic palsy, which usually appears within the first months of life and only in rare cases within the first weeks. It can be seen on a chest X-ray as an abnormal elevation of the dome of diaphragm. The eventration can affect either one or both hemidiaphragms, but it often starts on the right side of the chest, probably because of a pressure practiced on the diaphragm by the liver The eventration of the diaphragm can be very suggestive for SMARD1 when associated with one or more of the following characteristics newborn with respiratory distress, eventually associated with severe bronchopneumonia or any other kind of serious condition (such as near miss sudden infant death syndrome);family history of a sudden and inexplicable death of a previous child;consanguinity in parents;foot and hand muscle weakness and/or distal articular retractions.Intrauterine growth delay, prematurity and reduced foetal movements can often be associated with SMARD1. These same non-specific signs could be present not only in the affected patients but also in related siblings, where they could eventually be an indication of a form of late onset SMARD1 Unlike every other patient with SMA, muscular weakness in patients with SMARD1 involves the distal muscles at first and only later the proximal ones Advanced SMARD1 often presents a significant autonomic involvement. Symptoms can vary from cardiac arrhythmia, to urinary retention (with need of catheterization), bladder incontinence, excessive sweating and constipation et al., the EMG must show an acute or chronic distal denervation and a reduction in sensory or motor conduction velocities . On a sural nerve biopsy, histopathological abnormalities are a decreased size of the myelinated fibres, without signs of regeneration or demyelination From a neurophysiological point of view, following the diagnostic criteria defined by Pitt SMARD1 has a considerable variation in onset. In the majority of cases, there is an early onset in infancy, but many cases of juvenile form of SMARD1 have been reported Very recently, Hamilton and his group IGHMBP2 analysis revealed two mutations, one of which has never been described before.As for the genetic aspects, the et al., recently investigated an English family with two affected siblings with clinical features consistent with a recessive Charcot-Marie-Tooth type 2 (CMT2), whose genetic analysis revealed compound heterozygous mutations in the IGHMBP2 gene Cottenie IGHMBP2 gene were identified: a non-sense 5′ mutation and a 3′ frameshift mutation in the last exon of the gene, which mother and father were respectively heterozygous for.The onset of the disease was in the late childhood, with a slow progression: both siblings are still able to work, drive and walk with the assistance of a walking stick and a silicon ankle foot orthosis. Although the younger sister had a clinically milder form, they both presented bilateral foot drop, distal weakness, upper and lower limbs atrophy, absent reflexes, sensory loss in feet and hands, no cranial nerve involvement and a trombone-shaped tongue. Nerve conduction studies, as well as the sural nerve biopsy, demonstrated an axonal neuropathy. Chest X-ray was normal and there were no respiratory problems. This was essentially a typical form of CMT2. Using Exome sequencing, two compound heterozygous mutations in the IGHMBP2 was Sanger sequenced in a cohort of 85 recessive CMT2 families and 11 families turned out with IGHMBP2 mutations. The phenotype was characterized by childhood onset, mild glove and stocking sensory involvement and mild sensory and motor axonal neuropathy, as demonstrated by electrophysiological studies . Some cases presented mild scoliosis and other trombone-shaped tongues. None of the cases had respiratory problems nor recurrent airway infections or sleep apnoea.After this discovery, MFN2 mutations, the most frequent form of CMT2, which have a near complete loss of large myelinated fibres and widespread loss of the small ones, patients with IGHMBP2 mutations have only a reduction in density of the large myelinated fibres, while the small fibres are well preserved.Unlike patients with IGHMBP2-associated CMT2, SMARD1, as well as in carriers and controls. Results demonstrated that patient with CMT2 features had significantly higher IGHMBP2 protein levels than SMARD1, but lower than the controls. This indicates that clinical differences are linked to IGHMBP2 protein levels.As for the protein quantification, to establish whether the abundance of residual proteins correlated with the severity of the phenotype, IGHMBP2 was quantified in the fibroblasts and lymphoblasts of patients with IGHMBP2-mRNA levels, suggesting that truncated or defective proteins undergo posttranslational degradation.Despite the reduction in protein levels, both SMARD1 and CMT2 had normal IGHMBP2 gene is composed by 15 exons encoding a protein of 993 amino acids  The The Sμbp-2 protein is ubiquitously expressed and in neurons it is mainly localized in the cytoplasm, as well as in the nucleus, dendrites and axons Despite the nearly ubiquitous expression of the gene product Ighmbp2 (OMIM*600502), in SMARD1 the α-motoneurons are predominantly affected IGHMBP2 gene mutations related to SMARD1 phenotype was published by Grohmann et al., in 2001 et al., identified in five SMARD1 patients nine new mutations of this gene, in particular seven missense and two non-sense mutations. Interestingly, seven of those nine mutations were found at highly conserved residues of the supposed DNA helicase domain The first report of et al., through biochemical analysis, demonstrated that the RH3 domain is involved in the RNA binding process. Even if the N-terminal helicase domain alone is able to bind RNA, it seems that RH3 domain increases this activity, allowing it to operate as an ATP-dependent helicase, binding and unwinding DNA and RNA in a sequence specific manner. In fact, a truncation of the N-terminal sequence of the RH3 domain significantly reduces the binding affinity both with DNA and RNA As mentioned above, Sμbp-2 has both a RH3 domain and an ATPase/helicase domain. Fukita The RH3 domain could be found in more than 700 proteins, with the same structural sequences Arg-X-X-X-His and in association with other domains, such as DEAH helicase domains, ring-type zinc fingers nuclease domains and ATPase domains. Moreover, in the particular case of Sμbp-2, the segment between 638 and 786 residues where the end R3H C-terminal domain is located, binds ssDNA with 5′phosphorylated guanine rich sequences and it acts as an anchor. The N-terminal domain, on the other hand, interacts with the nucleotides located downstream Although a genotype–phenotype relation has not been identified yet, it has been observed that seven of the nine typical missense mutations influence the ATPase activity, either through the reduction in the structural stability or through the interference with the ATP binding/hydrolysis of the helicase domain In particular, the T4931 mutation seems to cause a neuronal degeneration through the reduction in intracellular Sμbp-2 protein levels IGHMBP2 gene have been described. Twenty-six new mutations in the IGHMBP2 gene were reported in children who suffered from a severe respiratory distress, because of diaphragmatic paralysis, and from a progressive muscle weakness, which appeared within 6 months of life. Those mutations included fourteen missense, six non-sense, four frameshift, one in-frame deletion and one frameshift insertion In the following years, an increasing number of new mutations in the IGHMBP2 gene. In particular, in the neuromuscular degeneration (nmd) mouse model an aberrant splicing near the 5′ end of the gene has been detected, which comprehends 23 additional nucleotides and a splice donor mutation (A to G) into intron 4, resulting in a premature stop codon. This last mutation interferes with the normal splicing of the mRNA, causing in about 75–80% of cases a mutation splicing and in the other 20–25% a wild-type splicing, not only in the brain and in the spinal cord but also in all other tissues as well Currently, little is known about the distribution and role of Ighmbp2 in the pathogenesis of SMARD1. Different types of mutations have been identified in the et al., investigated its localization inside the cells, through the use of different α-Ighmbp2 antibodies in primary embryonic mouse motor neurons, which are the first to be affected in SMARD1 As mentioned above, the Ighmbp2 protein is expressed ubiquitously in the organism, but, to better understand its function, it is important to analyse the subcellular localization. For this reason Guenther nmd mice it has been observed that the expression of the protein was extremely reduced in all those sites The results have shown that Ighmbp2 is mostly localized in the cytoplasm, in particular in the perinuclear cytoplasm, in the axon and in the growth cone. Moreover, in the et al., identified Reptin and Pontin, which are two ATPase/helicases proteins, the transcription factor IIIC 220 kD (TFIIIC220) and tubulin, which are able to co-immunoprecipitate with Ighmbp2. Further analysis has shown the real connection between Ighmbp2 and both Reptin and Pontin, as well as with TFIIIC220, but no interaction with tubulin. They also demonstrated the ability of Ighmbp2 to bind itself Another point worth considering, to further comprehend the role of this protein, is which proteins Ighmbp2 is able to bind to. Under this perspective, de Planell-Saguer Met and tRNAArg and probably also the 5′-ends of tRNAs. These data suggested that Ighmbp2 was also able to interact with the ribosome, but was not able to correctly translate certain mRNAs, which are important in motor neurons nucleic acid-binding domain Another study of the same group investigated whether Ighmbp2 was also involved in processing, regulation or metabolism of RNA, because of its helicase domain. They observed that Ighmbp2 could bind 3-end of both tRNAOverall, Ighmbp2 acts as an enzyme as well, unwinding RNA and DNA in an ATP-dependent reaction, supporting the theory that it shares not only sequence and structural similarities with the members of UPF1-like helicase within superfamily1, but also its helicase functions Moreover, Ighmbp2 is also able to hydrolyse ATP and distinguish among NTP co-factors Animal models are essential to understand the basic mechanisms and the physiology of the diseases. In particular, rodent models, natural and transgenic, provide the opportunity to test the efficacy of potential therapies and to obtain precious information to target treatments and improve gene and drug therapies http://www.jax.org/). The model used for SMARD1 is the nmd mouse, in particular the B6.BKS Ighmbp2nmd-2J mouse Different murine models are available for motor neuron diseases , that specifically halted motor neuron degeneration and rescued the nmd phenotype It has also been described the existence in the murine mammalian genome of a major wild-type modifier locus on chromosome 13  that has an agonist effect on the tyrosine kinase receptor C (trkC), which is involved in the regulation of neuron plasticity and synaptic strength Ruiz A possible explanation of these transient effects could be that high plasma levels of Mab2256 might suppress the expression of trkC, as demonstrated in other studies with neurotrophins (NTs) et al., et al., demonstrated that in the nmd model IGF1 serum levels were quite low, supporting the hypothesis of a linkage between IGF1 serum levels and muscular/neuronal degeneration nmd mouse. This happened through two different mechanisms: (i) increased phosphorylation of Akt (protein kinase B) and ribosomal protein S6 kinase, which protects from muscle fibre degeneration and (ii) action on sprouting of nerve terminals. However, no significant breakthrough was made in the motor neuron survival. A possible reason for this negative result is that the systemic dose used in this study was not able to reach a sufficient serum concentration to penetrate through the blood–brain barrier, thus making it impossible to reach an effective local tissue concentration.Also neurotrophic factors, such as insulin-like factor 1 (IGF1), have been considered as a possible candidate for future treatment trials, since they seem to play an important role in the pathogenesis of SMARD1, as recently demonstrated by Krieger Based on these findings, IGF1 and other neutrophic factors may hold promise as candidates for future studies in SMARD1 treatment.SMN1, encouraging a future application of gene therapy also in SMARD1.Gene therapy has the advantage to treat the cause of the disease, allowing the defective gene to be expressed. It is based on the utilization of viral vectors, which carry a healthy copy of the gene into the affected cells Fig. 40–42. TAs for SMA, gene therapy mostly focuses on replacing the defective gene using self-complementary adeno-associated virus vectors SMN gene was able to rescue the phenotype of SMA mice when injected intravenously www.clinicaltrial.gov).In fact, the discovery that the AAV9 vector was able to penetrate the BBB allowed considering the administration of AAV by less-invasive injection, such as systemic intravenous delivery and intrathecal delivery. The administration of AAV9 encoding wild-type At the moment, no clinical trial with gene therapy for SMARD1 was planned.Currently, SCs represent a promising resource for the treatment of neurodegenerative diseases, such as motor neuron disorders. Stem cells are characterized by their potential to continuously renew themselves by symmetrical division and to originate more mature progenitors of multiple lineages through asymmetrical division Our group previously demonstrated that murine NSC transplanted in a SMARD1 mouse model improved the animal phenotype hiSSClo) NSCs in nmd mice as a possible therapeutic approach for SMARD1. ALDHhiSSCloNSCs cells derived from both embryonic and adult spinal cord and were self-renewing and multipotent. They were able to differentiate into the three main lineages and also to acquire mature complex neural phenotypes, including the expression ofHB9/ChAT in motor neurons, in nmd mice both in vivo and in vitroIn our first set of experiments, we transplanted ALDH high side scatter low -derived NSCs, including glial-derived neurotrophic factor, brain-derived neurotrophic factor, transforming growth factor-α and NT3 These experiments of NSCs transplantation were performed with primary murine NSCs. The discovery of iPSCs allowed to obtain unlimited human cells, like NSCs, overcoming the necessity to obtain them from CNS.ex vivo gene correction and use the corrected cells as potential autologous cell source for transplantation  and HPK1/GCK-like Kinase (HGK). These results prove that the therapeutical use of NSCs derived from pluripotent SCs can be considered as a valid tool to improve SMARD1 phenotype. Another interesting possibility is represented by the possibility to convert the disease phenotype of iPSCs derived from SMARD1 patient in healthy iPSC through an tion Fig..We believe that what would allow to reach a clinically significant level in SMARD1 (and other motor neuron diseases as well) in terms of therapeutic efficacy would be the combining of cell, drug and gene therapy Musculoskeletal diseases are the second-greatest cause of disability worldwide The pathomechanisms of SMARD1, and in particular the reasons behind the vulnerability in specific motor neuron subsets, is unknown, but addressing this question might allow to define novel therapeutic targets for SMARD1.Despite the absence of a resolutive therapy, different approaches have been tested, but only at the pre-clinical level, including, pharmacological treatment, gene therapy and cell therapy.Particularly interesting appears to be the benefits observed with neurotrophic factors, that may warrant further investigation. However, the limit of this approach can be the difficulty in their delivery to CNS as well as the possible systemic side effects.Gene therapy seems one of the most appealing curative approaches, given their translatability in human. The definition of the therapeutic window in this case will be one of the most important aspects. As for the cellular approach, increasing evidence suggests that NSC transplantation can exert a therapeutic effect on motor neuron disease phenotypes. Our group contributed in studying the utility of this approach in SMARD1. Further studies need to be performed, to define the best cell source, the modality of administration and its safety as well as the extent of the therapeutic impact that can be achieved with this approach.Combinatory approaches of pharmacological, gene therapy and cellular strategies can be envisioned of the development of effective therapeutic strategies for SMARD1 and other genetic motor neuron diseases.


Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing. To identify disease variants that occur less frequently in population, sequencing families in which multiple individuals are affected is more powerful due to the enrichment of causal variants. An important step in such studies is to infer individual genotypes from sequencing data. Existing methods do not utilize full familial transmission information and therefore result in reduced accuracy of inferred genotypes. In this study we describe a new method that infers shared genetic materials among family members and then incorporate the shared genomic information in a novel algorithm that can accurately infer genotypes. Our method is particularly advantageous when inferring low frequency variants with fewer sequence data, making it effective in analyzing genome-wide sequence data. We implemented the algorithm in a computationally efficient tool to facilitate cost-effective sequencing in families for identifying disease genetic variants. DNA sequencing is being routinely carried out to identify genetic factors, rare variants in particular, associated with human disease. It has been successful in identifying causal variants for Mendelian disease ,2, and ca priori. Polymutt and FamSeq assume such a priori probabilities for all genomics regions and thus inefficiently model the data when the actual IBD can be inferred. As a concrete example, assuming we know that at a particular position the two siblings share 2 alleles IBD, then their genotypes are identical at this locus and can be inferred with improved accuracy by merging their sequencing data, essentially doubling the sequencing depth. In general, knowledge of IBD sharing helps confine genotypes to be compatible to the IBD patterns in pedigrees and a variant calling framework that models IBD is expected to deliver improved performance over existing methods. Such a framework makes it feasible to design studies with reduced coverage, since data from shared haplotypes in a pedigree are efficiently combined to make reliable genotype calls. This will be particularly beneficial for whole genome sequencing, which is still prohibitively expensive for large-scale sequencing studies. Linkage-disequilibrium (LD)-based methods such as Beagle4 [2) among rare variants as well as between rare and common variants, however, LD-based methods are expected to have reduced accuracy for rare than for common variants.A critical step in genetic analysis of family sequence data is to infer genotypes of individuals in pedigrees. For next-generation sequencing data, this is challenging due to base call error, alignment artifacts, possible allele dropout during library preparation and sequencing, especially at low coverage, among others. Although variant calling algorithms developed for unrelated individuals can be applied to family sequencing data, the accuracy is compromised due to the ignorance of family relatedness. Family-aware calling algorithms, e.g. Polymutt  and FamS Beagle4 , Thunder Beagle4  and SHAP Beagle4  have beeIn this study, we develop and implement a variant calling framework that infers the IBD sharing   file, whf founders and n non-founders, with sequencing data on M variants across a chromosome. Without loss of generality, we also arrange the pedigree such that the first f members in the pedigree are founders. Define a binary inheritance vector [j as Ij=  for the n non-founders in this pedigree. Each of the entries describes the transmission of the paternal (pi) or the maternal (mi) allele, with 0 (or 1) indicating the grand-paternal  allele being transmitted. Therefore an inheritance vector completely determines which of the 2f founder alleles were inherited by each nonfounder. There are N = 2n possibility inheritance vectors and let vk, k = 1,..,N, denote individual vectors. Let R denote data of all members in a family across all M variants, Rj be all reads at variant j, and Rij be the reads in family member i at variant j. Similarly, let Gj denote the vector of genotypes at variant j and its ith entry Gij =  be the ordered genotype of the ith member in the pedigree, where A1 and A2 represent the paternally and maternally transmitted alleles respectively. Assuming that recombination events are independent between all chromosome intervals, i.e. no crossover interference, the likelihood can be framed as a Hidden Markov Model [M variants in the pedigree can be calculated asP(I1), is assumed uniform across all N = 2n inheritance vectors. The transition between adjacent inheritance vectors, P(Ij|Ij-1), is calculated according to the recombination rate between the jth and j-1th variant, which can be calculated using the HapMap Phase II [P(Rj|Ij), which is the probability of reads in all family members at locus j given the inheritance vector Ij, can be calculated asP(Rj|Gj) can be factorized into the product of individual genotype likelihoods. Since an inheritance vector specifies precisely how the alleles were transmitted from founders to non-founders, the genotypes of the entire pedigree are determined by the ordered founder genotypes when the inheritance vector is known. Therefore the emission probability involves the summation of only the ordered founder genotypes, whose prior probabilities P(Gij) can be either obtained from the external sources, e.g. the 1000 Genome Project [Suppose we have a pedigree with e vector  at variaov Model , similarov Model . SpecifiPhase II  genetic  Project , or esti Project , assuminM variants; that is, we aim to calculate P(Ij|R). This can be achieved efficiently using the forward-backward procedure in HMM [αj(k) denote the forward variable at variant j for vk and βj(k) be the corresponding backward variable [vk at variant j isj, denoted as Ijmarg, can be used to represent the inferred inheritance vector for each variant; the IBD sharing can be directly derived from Ijmarg for any pair of family members. However, since Ijmarg only maximizes the likelihood marginally at variant j, we infer a global optimal path of inheritance vectors along the genome through the Viterbi algorithm [Ijbest to denote the optimal inheritance vector at variant j.Since the inheritance vectors usually cannot be determined unambiguously, the goal here is to infer the posterior distribution of the inheritance vectors at each variant using the sequencing data from all e in HMM . Let αjvariable . Then thlgorithm ; we use http://www.genome.ucsc.edu) and the 1000 Genomes Project. The final set of selected variants is used to contrast a sparse genetic map with high-quality variants for the inference of inheritance vectors, and the genetic distances of these variants are linearly extrapolated based on the HapMap Phase II genetic map [The Lander-Green algorithm requires that variants are independent, i.e. not in linkage disequilibrium (LD). For sequencing data variants are usually correlated. Since only a limited number of recombination events are expected in a pedigree, it is neither feasible nor necessary to use all data. We built a companion tool to automatically select a subset of independent variants by LD pruning, a similar approach used in Plink and others ,27. In aetic map . The oveP and P, representing the likelihood of polymorphism and monomorphism respectively given the data at the jth site and scaffold variants. We assume that for poly the two alleles are Aref and Aalt and for mono only Aref is present in the data. The posterior probability of polymorphism given the data is calculated asWe evaluate for each variant the evidence supporting the alternative allele in the data by calculating the posterior probability of being polymorphic. Specifically for each variant we calculate two probabilities, P is calculated based on P is simply the product of genotype likelihoods of homozygous reference allele across all family members at variant j. The prior probability of polymorphism, P(poly), is calculated as in Polymutt [P(mono) = 1-P(poly). Briefly, in a sample with N founders in the absence of natural selection, according to coalescent theory the prior probability that a site includes non-reference alleles is j is one of the scaffold variants, P = P(Ij | R), which was obtained in (2). Then the Phred-scaled variant quality is calculated as VQ = -10*log10). By construction, only a sparse subset of variants is included in the map file, and the vast majority of variants are located in the intervals of scaffold variants. In an interval within which a crossover occurred, the inheritance vectors on the two sides of the recombination point are different. Assigning wrong inheritance vectors to variants will not only produce wrong IBD sharing among family members but also greatly reduce variant calling accuracy. However it is unknown a priori in which intervals crossovers occurred and where exactly the breakpoint is if a crossover occurred. To address this issue, Polymutt2 calculates for each variant in scaffold intervals the posterior probabilities using the inheritance vectors on the left and right boundary separately, and takes the maximum value, Pmax, as the posterior probability of polymorphism. The Phred-scaled variant quality is calculated as VQ = -10*log10). The inheritance vectors are accordingly assigned to each of the variants in scaffold intervals. This assumes that there is at most one crossover event in any interval, which is reasonable given the limited number of expected recombination events per generation. As a result, the crossovers can be precisely located in intervals in which crossovers occurred.The term Polymutt  and P can be calculated using Gij = g. For variants in intervals of scaffold variants, P was obtained in calculating the variant quality as described in the previous section. The genotype with the maximum posterior probability Pmax is assigned to the individual, and the corresponding genotype quality is calculated as GQ = -10log10).After quantifying the variant quality as described above, the most likely inheritance vectors are assigned to each of the variants. The posterior probability of the genotypes for individual Ijbest or Ijmarg, can used to minimize the computation. Given the high accuracy of the inheritance vector inference . At the extreme, a single best inheritance vector, ence see , the incIjbest as the optimal inheritance vector for variant j inferred using the Viterbi algorithm as we described before. The posterior probability of each configuration of ordered genotypes at variant j given the sequencing data and Ijbest is calculated asP(Rij|Gij) is the genotype likelihood calculated before, and the term Ijbest. This is repeated for all positions and the haplotypes are automatically constructed by stitching the paternal and maternal alleles at each position along a chromosome. By construction, Mendelian error in haplotype calling is completely eliminated due to the Mendelian transmission dictated by the inheritance vector.When parental data available, we generate haplotypes along a chromosome by reconstructing the optimal ordered genotypes jointly for all family members at each position assuming that the inheritance vector is known. We use Note that the construction of haplotypes is based on the transmission of alleles from parents to offspring. When parental data are not available, such as in sibships, it is not possible to deduce the parental origin of the alleles and therefore haplotypes cannot be reconstructed.http://picard.sourceforge.net). The list of known Indels from the 1000 Genomes Project was provided to GATK for Indel re-alignment. We used GATK UnifiedGenotyper to infer variants and genotypes from sequencing. We then applied Polymutt, Polymutt2 and Beagle4 on the GATK-generated VCF files to refine the genotypes utilizing the GL values calculated by GATK and stored in the VCF file.We utilized the 1000 Genome Project  data to Pedigrees we investigated in this study include sibships of size 2 (Sib2), 4 (Sib4) and 6 (Sib6), nuclear families with 4 (Nuc4) and 6 (Nuc6) members, and an extended pedigree with 10 individuals, which is the same as the pedigree investigated in Polymutt . For eacWe used two metrics to measure the accuracy of genotype calling. The first is the false negative rate (FNR), defined as the percentage of true genotypes that are called into incorrect genotypes; this measures the sensitivity of the calling and is equal to 1-sensitivity. The second metric is the false discovery rate (FDR), defined as the percentage of called genotypes that are different from the true genotypes; this measures the specificity of the calling; this measures the specificity of the calling and corresponds to 1-precision. A good algorithm is expected to have low values of both FNR and FDR. We used GQ to filter low quality genotype calls and specifically we used GQ = 3 for Polymutt2 and Polymutt, GQ = 5 for Beagle4 and GQ = 10 for GATK; due to different calculations of GQ in these algorithms we found that these filtering criteria have reasonable FNR and FDR values. These criteria were used for all simulated data. Note that when no filtering is used FNR and FDR are the same for overall genotypes, and the difference is due to differential filtering based on GQ cutoffs. For heterozygotes, which are of particular interest in studying rare variants, however, both FNR and FDR are critical metrics to evaluate, as FNR can be made artificially low by aggressive calling of heterozygotes, which will results in high FDR, and conversely conservative calling of heterozygotes can lead to low FDR and high FNR.We derived the IBD sharing at each position between a pair of family members in a pedigree based on the inferred inheritance vector obtained via the Viterbi algorithm. We next investigated the accuracy of the heterozygous genotypes, which are of particular interest for rare variants. The major interest of sequencing studies, especially in family designs, is to identify rare variants associated with disease. Accurate heterozygote calling is of particular importance due to the challenges associated with rare variant inference from sequencing. We specifically investigated the heterozygote accuracy across different bins of alternative allele frequencies, in the range of  is similar to the unphased genotypes, although on average the error rates are slightly higher for phased genotypes. For example, for Nuc4 pedigrees at 15X, the FNR of overall genotypes is 0.30% for phased genotypes and is 0.28% for unphased genotypes; the corresponding FDR is 0.09% and 0.10% respectively. For heterozygotes, the FNR is 0.53% and 0.51% for phased and unphased genotypes respectively, with corresponding FDR values being 0.10% and 0.08%.–6 for all scenarios shown above, consistent with the previous report [We calculated the Mendelian inconsistency (MI) rate as the percentage of parent-offspring trios in which the genotypes violate the Mendelian transmission law. Pedigrees were divided into individual trios for the calculation. We used the minimum GQ of the genotypes in a trio as the filtering criteria to calculate MI rates on relatively high quality genotype calls. When either GQ 5 or 10 was used, both GATK and Beagle4 calls showed considerable Mendelian inconsistencies across various sequencing coverage . For exas report . Strikinhttp://www.illumina.com/platinumgenomes/). We selected a Nuc6 sub-pedigree for the analysis, which consists of four siblings  and their parents (NA12877 and NA12878). The sequencing coverage of these samples is ~50X. We followed the best-practice procedure for variant calling as we did for simulated data. Since there is only a single family with a few individuals, we used the 1000 Genomes Project reference panel when running Beagle4 (downloaded from Beagle4 website) on this pedigree to leverage the extensive LD in the panel. To have a fair comparison of Polymutt2 with Beagle4, we ran Polymutt2 using the allele frequencies derived from the same reference panel. Based on simulation results it is clear that at high coverage over 30X the accuracy measures of all callers are satisfactory. Our major goal here is to investigate to what extent genotype calls from various callers with a subset of data can recover the original high depth sequencing data. We first created a gold-standard callset from the original high-depth data by taking the consensus of genotype calls from GATK, Polymutt, Polymutt2 and Beagle4; this call set contains genotypes that are agreed by all 4 callers. GATK and Polymutt infer allele frequencies from the sequence data only, and due to the small sample size of the pedigree the estimates are not reliable. Here we focused only on the comparison of Polymutt2 and Beagle4, two competing methods based on simulated data. Specifically, we randomly extracted 30% and 15% of the reads from the original alignment, corresponding to ~15X and ~7.5X of coverage, and carried out variant calling using both Polymutt2 and Beagle4. For each of the two callers we calculated FNR and FDR using the gold-standard callset. We also compared their performance stratified by allele frequencies, which were calculated based on the same reference panel used in Beagle4. Since genotype filtering has a strong impact on FNR and FDR, e.g. aggressive filtering results in low FDR and high FNR and vice versa, we calculated the two measurements using GQ values from 3 to 30 and plotted them in FNR-FDR curves to represent genotype accuracy with a wide range of filtering. This is an objective way of comparing genotype accuracy and a curve completely underneath the other indicates consistent high accuracy of genotype calls for all GQ cutoffs in the range of 3 to 30.We downloaded the whole genome sequencing data in CEPH pedigree 1463 generated on the Illumina HiSeq platform , Polymutt2 calls achieve greater concordance with the gold standard callset than Beagle4, as manifested by the reduced error rates in the FNR-FDR curves . When weWhen we focused on heterozygotes, Polymutt2 and Beagle4 calls have similar accuracy when all variants were considered with both 30% and 15% of the data  Fig. WheWe observed considerable MI rates in Beagle4 calls with both ~15X and ~7.5X data. For example, at ~15X the MI rates are 0.14% and 0.09% when the minimum GQ was set to 5 and 10, respectively. The corresponding MI rates at ~7.5X are 0.15% and 0.1%. When we focused on low frequency variants, the MI rates in Beagle4 calls are noticeably increased. For example, for variants with allele frequency <0.1 at ~15X, the MI rates are 0.27% and 0.21% for GQ cutoffs of 5 and 10, respectively, indicating that increased genotype error rates associated with low allele frequencies in Beagle4 calls resulted in higher MI rates. On the other hand, as a direct comparison, we did not observe Mendelian error in Polymutt2 calls in all of these scenarios investigated, indicating the extremely low Mendelian error rate in Polymutt2 calling in real data.Sequencing pedigrees has shown its effectiveness in identifying rare variants associated with human disease, and is expected to continue in gene mapping for complex traits in complement to population-based designs. In addition, family designs are not prone to population stratification, which may be more challenging to control for rare variants . In this2 in this context) between rare variants and between rare and common variants is low, the effectiveness of LD-based calling for rare variants is reduced. Although for pedigrees with limited IBD sharing (e.g. sibpairs) Beagle4 outperformed Polymutt2 when considering all genotypes, Polymutt2 still achieved increased accuracy in calling rare variants. In addition, Mendelian inconsistency in LD-based calls, especially for rare variants, which are usually analyzed in groups, may have inadvertently impact on association analysis since the effect of Mendelian error in individual variants may be aggregated and amplified. As the major focus in sequencing is to identify rare variants we hope that Polymutt2 is useful for gene mapping of rare variants for complex disease.Compared to Polymutt and GATK, Polymutt2 has increased accuracy of genotype calling from all aspects. This is rather unsurprising given that Polymutt2 uses extra information than the other two callers. On the other hand, Polymutt2 and Beagle4 use orthogonal information, i.e. the explicit modeling of IBD sharing in Polymutt2 vs. the utilization of LD among variants in Beagle4 for variant calling. Since the LD  to O(NlogN)in the HMM, where N is the number of inheritance vectors. Even with these speedup techniques, however, the computation can be still very challenging. To further mitigate the problem, we implemented the software using multi-threads so that the computation can be parallelized when possible. The current implementation can handle simple pedigrees efficiently. For example, for sibpairs and sibships of size 4, and nuclear families of size 4 and 6, the average time per family using 8 threads for chromosome 1 whole genome sequencing is on the scale of minutes. For sibships of size 6 the time is significant increased and it took over an hour to finish variant calling per family. For pedigree of Ext10 the time is even further increased to over 10 hours to get marginal calls. If computing is limited an option is to use only inheritance vectors with highest posterior probabilities for such pedigrees; for example using the single best inheritance vector the computing is a few minutes. For pedigrees beyond the exact calculation of the likelihoods, Monte Carlo approaches [Since the Lander-Green algorithm is the major component for the inference of the inheritance vectors, the computation is linear with respect to the number of variants but can be explosive when pedigrees get large. For a pedigree with  storage . Further storage , which rproaches –35 are n2 = 0.2. We also investigated other thresholds to evaluate the sensitivity of the results to the LD pruning. Specifically we used cutoffs of 0.1 and 0.5 and observed similar results as 0.2, with the difference below 0.01% for most of pedigrees and coverage investigated in Figs In the inference of inheritance vectors, we selected the scaffold variants by LD pruning. The results reported in the article were based on the maximum correlation coefficient of RWith the comprehensive catalog generated by the 1000 Genomes Project, identifying known variants in study sample is generally very accurate. However calling novel variants for pedigrees is usually of particular interest. This remains challenging due to potential alignment artifacts. Unannotated structural variants are a major source of alignment artifacts, and when such artifacts do not follow Mendelian transmission laws the variant quality is expected to be dramatically reduced for such sites when the IBD sharing is imposed in the calculation of the likelihood. We believe that Polymutt2 is effective in filtering false novel variant candidates given its efficient use of allele sharing.De novo mutations, however, violate the rule and make the inference of inheritance vector inaccurate. Although it is unlikely to include de novo mutations in scaffold variants, accidental inclusion of such variants makes the results not reliable. To avoid this situation, Polymutt2 internally checks the likelihood of de novo mutations during the calculation and if a strong violation of Mendelian inheritance is detected the algorithm ignores these variants so that the inheritance vectors can be robustly inferred. The current version of Polymutt2 is not designed to call de novo mutations and other methods  should https://medschool.vanderbilt.edu/cgg). We hope that our user-friendly software packages are useful to the research community for pedigree sequencing studies to facilitate the identification of rare variants for human disease.Our tools were implemented in C++. The source code and company resources can be downloaded from the authors’ website  for sequencing coverage of 10X and 20X, and panels C and D show the FDR (%) for the same set of coverage.(TIF)Click here for additional data file.S2 FigResults of different pedigrees are shown in panel A) for Sib2, B) for Sib4, C) for Sib6, D) for Nuc4, E) for Nuc6 and F) for Ext10.(TIF)Click here for additional data file.S3 FigApplication to real data for the details on the pedigree and the calculation of error rates.See (TIF)Click here for additional data file.