Neurodegenerative disorders affect millions of people worldwide, and this number is expected to increase substantially with rising life expectancy and population growth. Major efforts have been directed toward understanding the etiology of neurodegenerative conditions, but the challenge remains to elucidate molecular mechanisms underlying the onset and progression of neurodegeneration. Recent studies have identified multiple genetic mutations and risk factors associated with dementia and neurodegeneration, but additional causes are likely involved (Fu and Ip, 2023; Pihlstrøm et al., 2017; Bertram and Tanzi, 2005; Hardy and Orr, 2006). Aging is a major risk factor for many neurodegenerative disorders and plays a key role in disease emergence and progression (Azam et al., 2021; Saikumar and Bonini, 2021). Other significant risk factors include environmental insults that can evoke neuropathological processes, which may be latent at first but surface with aging to disrupt various aspects of brain structure and function, leading to neurodegenerative conditions (Brown et al., 2005; Gupta and Sen, 2016). Physical blows to the head are known to cause symptoms related to immediate brain injury, but even seemingly innocuous head impacts are strongly linked to brain dysfunction and degeneration later in life (Gupta and Sen, 2016; Brett et al., 2022; Ntikas et al., 2022). It is thus crucial to understand the aberrant processes triggered by these mild physical insults that may be initially ‘hidden’ but can give rise to later neurodegenerative conditions. Furthermore, sex differences have been documented in many degenerative diseases, including chronic brain degeneration following mild traumatic brain injury (Yue et al., 2019; Berz et al., 2013; Gupte et al., 2019). How sex and age contribute to an individual’s response to mild brain disturbances and modify the risk for neurodegeneration remains to be fully understood.

The fruit fly, Drosophila melanogaster, represents an excellent and tractable model organism for dissecting fundamental disease mechanisms, including neurodegeneration (Hirth, 2010; Lessing and Bonini, 2009; Petersen and Wassarman, 2012; McGurk et al., 2015). Importantly, the fruit fly’s relatively short lifespan enables longitudinal interrogation of disease progression from the initial trigger to the late-life emergence of neurodegenerative conditions such as Alzheimer’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia (McGurk et al., 2015; Pandey and Nichols, 2011; Yuva-Aydemir et al., 2018; Bolus et al., 2020; Prüßing et al., 2013). Recently, several Drosophila head injury models have been developed that recapitulate key findings from traumatic brain injury (TBI) in humans and other preclinical models, revealing significant insights into the potential molecular and genetic underpinnings of injury responses (Barekat et al., 2016; Katzenberger et al., 2013; Sun and Chen, 2017; Saikumar et al., 2020; van Alphen et al., 2022; Ye et al., 2023; Behnke et al., 2021b). We recently developed a novel Drosophila model (HIFLI: Headfirst Impact FLy Injury) in which mild repetitive head-specific impacts can be delivered to multiple awake and unrestricted adult flies of both sexes (Behnke et al., 2021b). In this study, we utilized the HIFLI model to deliver a milder version of repetitive head trauma, which eliminates potentially confounding effects from injury-induced death and enables us to interrogate sex and age-at-injury contributions to the emergence of brain deficits throughout the entire lifespan. Our data show that exposure to this milder form of head trauma elicits minimal acute deficits but causes profound brain-associated behavioral deficits and brain degeneration that only emerge later in life. These late-life neurodegenerative conditions are further exacerbated by increasing age-at-injury and disproportionately elevated in mated females. We further identify that sex peptide signaling involved in female reproduction plays a key role in elevated female neurodegeneration after mild head trauma. Finally, RNA sequencing data suggest that the chronic suppression of innate immune defense networks in mated females may mediate the elevated vulnerability to neurodegeneration after mild head trauma. Together, our results further establish Drosophila as an excellent model system to investigate lifelong neurodegenerative conditions, highlight that sex and age significantly contribute to trauma recovery and outcome, and support the notion that physical insults to the head, even very mild ones, pose a major threat for brain health.

An emerging body of evidence suggests that early exposure to repetitive mild head trauma increases the risk of developing neurodegenerative conditions later in life. However, the underlying mechanisms remain unclear. In this study, we take advantage of the short lifespan and powerful genetic tools of D. melanogaster, in combination with our headfirst impact model (Behnke et al., 2021b), to investigate how early exposure to a very mild form of repetitive head trauma leads to late-life neurodegeneration. Previously, we showed that mild repetitive injuries can elicit degenerative phenotypes in both male and female flies, but female flies appear more vulnerable to the injuries (Behnke et al., 2021b). Our current study has further substantiated the previous findings by examining different age-at-injury cohorts and demonstrating their age-dependent and sexually dimorphic responses to a very mild form of repetitive head impacts. Furthermore, we discovered a strong link between SP signaling and injury-induced neurodegeneration in females. Compared to males, virgin females, and SP⁰-mated females, female flies mated to wildtype males were much more vulnerable to developing sensorimotor deficits and brain degeneration following injury exposure. This demonstrates, for the first time, the convergence and interaction of two supposedly separate pathways, namely the pathophysiological mechanisms elicited by mild head injury and the post-mating responses mediated by SP exposure. While activation of either one of these pathways was insufficient to generate neurodegenerative effects in females, their combination facilitated the emergence of quantifiable brain deficits. These findings provide an important foundation to further investigate the molecular and cellular mechanisms underlying sex differences in injury responses and neurodegeneration.

Sex-related differences have been documented in many medical conditions, including neurodegenerative disorders (Vegeto et al., 2020; Cerri et al., 2019; Nebel et al., 2018; Podcasy and Epperson, 2016) and neurotrauma of various severities (Yue et al., 2019; Gupte et al., 2019; Rauen et al., 2021). Sex differences include anatomical differences, sex gonadal hormones, and immunological responses, all of which presumably affect vulnerability to injury. In fact, there seems to be a growing consensus concerning sex-dependent effects in response to TBI, though clinical and preclinical findings are inconsistent. While female sex is more often associated with worse outcomes in human TBI studies, in preclinical TBI studies it is more frequently associated with better outcomes and neuroprotection after TBI (Gupte et al., 2019). Part of this disparity may be attributed to differences in injury severity and animal model, but the underlying drivers of sex differences in brain injury responses and neurodegenerative conditions remain to be fully elucidated. While sex differences in insects inherently differ from that in humans, there are some similarities associated with reproduction and hormone regulation. Therefore, it is likely that some of the fundamental mechanisms revealed in Drosophila may be conserved in humans.

Mating elicits two major types of changes in female flies; increase in egg-laying and reduction in receptivity to mating (Liu and Kubli, 2003; Chapman et al., 2003). Sperm alone can induce a short-term post-mating response, but a sustained response (>7 days) requires SP (Liu and Kubli, 2003). SP signaling via SPR activation is believed to switch the female to a state that maximizes reproduction (Yapici et al., 2008), which is considered evolutionarily beneficial. However, SP exposure can jeopardize female survival by decreasing daytime sleep (Isaac et al., 2010; Garbe et al., 2016), altering the immune system (Schwenke and Lazzaro, 2017; Short and Lazzaro, 2010; Short et al., 2012; Peng et al., 2005), and increasing female susceptibility to age-dependent tumors (Regan et al., 2016). To our knowledge, the current study is the first to directly show that SP-signaling can elevate vulnerability to injury-induced neurodegeneration in female flies. While mammals do not have SP, reproduction in females is known to dramatically alter metabolism, immune system, hormones, and neurobiology, all of which are vital for maintaining pregnancy. A connection between increased risk for neurodegeneration and hormonal changes associated with reproduction has been suggested in humans and other mammals (Xiong et al., 2022; Gilsanz et al., 2019; Jang et al., 2018). There are also contradictory findings, where female sex hormones are found to be neuroprotective (Ha et al., 2007; Song et al., 2020). These findings highlight the need for further animal studies with better controls of hormonal and reproductive cycles.

The current study also examines how aging affects response to mild head injury. In humans, age is one of the strongest outcome predictors for complications following head trauma, including mild trauma (Moretti et al., 2012; Jacobs et al., 2010; Bittencourt-Villalpando et al., 2021). Though the cause of injury may differ, older age is consistently associated with an increased incidence of head trauma, a slower overall recovery process, as well as greater morbidity and mortality following injury (Abdulle et al., 2018; Mosenthal et al., 2004). Consistent with these observations, we show that flies exposed to vmHT at older ages developed neurodegenerative deficits at a much faster rate than flies injured at younger ages. Our finding that aging accelerates injury-induced neurodegeneration is also supported by findings in humans and other animal models (Doust et al., 2021; Rowe et al., 2016; Ojo et al., 2013), suggesting conserved pathways underlying aging-induced vulnerability. Previous studies have suggested that mitochondrial function (Cui et al., 2012; Chistiakov et al., 2014) and immune function (Weyand and Goronzy, 2016; Haynes, 2020) decline with aging. Given that mitochondrial dysfunction (Cheng et al., 2012; Kim et al., 2017) and compromised immune responses Needham et al., 2019; Verboon et al., 2021 have also been implicated in TBI-induced brain dysfunction and degeneration, future work using our mild injury paradigm should investigate their contributions to aging-increased vulnerability and the development of neurodegeneration in response to head trauma.

Sex differences in aging are conserved across species. In line with this, we demonstrated sex differences in aging-associated vulnerability. While increasing age-at-injury in mated females is generally associated with more severe neurological deficits, females injured on D17 exhibit similar degrees of climbing deficits and vacuolation to those injured on D31, suggesting a possible plateau (Figure 3A). On the other hand, male flies are very resilient to vmHT at younger ages (D3 and D17), displaying little to no phenotypes even on D45 (Figure 3A). However, at the advanced age of 31 days post-eclosion, exposure to vmHT drastically accelerates neurodegeneration in the male brain, resulting in deficits similar to that of mated females of the same injury group (Figure 3B and E). As flies age, male flies seem to show a larger decline in resistance to injury-induced neurodegeneration than females. This finding is supported by work studying sex differences in immune senescence (Kubiak and Tinsley, 2017). In males, this is believed to occur principally due to age-related deterioration in barrier defenses, whereas in females this largely manifests in decreases in innate immunity. Older males may have a weaker exoskeletal defense system, making them more vulnerable to the effects of injury.

To gain some insights into the molecular mechanisms underlying the sexually dimorphic, late-life emergence of neurodegenerative conditions after early exposure to vmHT, we surveyed the transcriptomic landscapes of fly brains with and without vmHT exposure. We were specifically interested in identifying genes whose changes in expression might underlie the observed sex differences between male and mated females, as well as between females of varying reproductive status. Across all conditions and timepoints, we found that the injuries (regardless of age) elicited a very small number of differentially expressed genes that skewed toward decrease in expression. This was initially surprising, because previous Drosophila TBI work using the same RNA extraction protocol and sequencing company had reported large numbers of both immediate and lasting gene activation (Byrns et al., 2021). When we compared sham males and females at the acute timepoint, we found robust sex-specific gene expression (data not shown) and distinct clustering by sex in our PCA (Figure 6B). This confirmed the validity of our data and subsequent analyses and suggested that the small number of differentially expressed genes was likely due to the mild nature of our injury paradigm. Furthermore, it is possible that not all flies experienced increased neurodegeneration following the mild injuries, which could mask transcriptomic changes due to injury-induced neurodegeneration. Unlike other work utilizing a more severe injury paradigm (Byrns et al., 2021), we did not find any acute gene changes that persisted until the very chronic timepoint of 6 weeks following injury, suggesting that mild injury may induce dynamic transcriptomic changes throughout a fly’s lifespan. Additionally, cellular mechanisms that directly contribute to the development of neurodegenerative conditions may not be immediately active following injury; instead, they may be revealed by aging. To better understand the connections and interactions between acute and chronic changes in the transcriptome, future studies may further characterize these dynamic changes at multiple timepoints throughout the fly’s lifespan.

Previous work using a mild injury model has shown that AMP expression is elevated in the brain immediately following injury (in the first 12 hr), returned to baseline at 24 hr post-injury, and then elevated again 1 week post-injury (Barekat et al., 2016). This suggests that persistent elevation of AMP expression levels and the activation of the innate immune system at a chronic timepoint contributes to neurodegeneration. For both mated females and males, our results comparing injured and sham flies at the acute timepoint (1 dpi) do not show any elevation of AMP or AMP-like genes, which is congruent with previous data. A small percentage of immune-related gene changes were identified at this time, but these changes were all biased towards an injury-induced suppression. However, at 6 weeks post-injury, we find that the injury paradigm overwhelmingly depressed AMP and AMP-like gene expression in both male and mated female brains but not in virgin and SP-null mated female brains. Interestingly, injury-induced suppression of Metchnikowin and Diptericin and increased expression of PPO2 observed at the chronic timepoint is correlated with female-specific late-life behavioral deficits and brain pathology. This finding is supported by a recent report that mated female flies exhibited decreased markers of oxidative stress following trauma, which could be due to immune suppression following a TBI-induced immune challenge (Jones et al., 2024). Additionally, work comparing bacterial infection in virgin, SP⁰-mated, and wildtype-mated females suggests that mating and particularly egg-laying reduce the female’s overall ability to defend against bacterial infections, leading to decreased expression of AMP genes after infections (Short et al., 2012). This process is mediated by increases in Juvenile Hormone (Schwenke and Lazzaro, 2017), which is known to suppress innate immune processes (Flatt et al., 2008). SP exposure also acutely activates the Toll-like receptor and immune deficiency (Imd) pathways in females, altering levels of Metchnikowin and Diptericin (Peng et al., 2005). Furthermore, even one instance of mating is sufficient to induce chronic immunosuppression (Gordon et al., 2022). These changes may compound injury-induced changes to disrupt immune homeostasis, leading to observed immunosuppression in mated females. At an advanced age, a decreased expression of AMPs suggests that the mated female fly’s innate defense may be worsened by injury, making them unable to mount a sufficient immune response to pathological insults and therefore more susceptible to infections and disease.

Our finding of the decreased expression of AMP genes at this very chronic timepoint of 6 weeks post-injury, in combination with other groups’ findings of short-term elevation of AMP genes, also suggests a temporally dynamic gene expression pattern in response to head injury. Though immune suppression in mated females seems to contribute to neurodegenerative phenotypes, it is not immediately clear why suppression of a different set of AMP genes was not sufficient for neurodegenerative phenotypes in males. Future work can utilize males injured at older ages or with a more severe paradigm to understand male-specific transcriptome changes that contribute to injury-induced neurodegenerative conditions. Finally, though our RNA-seq data is informative, we acknowledge that there is limited correlation between mRNA and protein levels. Clearly, future studies are needed to dissect the genetic components and molecular players involved in the sex-different development of neurodegenerative conditions after mild head trauma.

RNA sequencing data have been deposited in GEO under the access code GSE274136.

1. 1. Ye C
   2. Ho R
   3. Moberg KH
   4. Zheng JQ

   (2024) NCBI Gene Expression Omnibus

   ID GSE274136. Adverse Impact of Female Reproductive Signaling on Age-Dependent Neurodegeneration After Mild Head Trauma in Drosophila.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE274136

1. 1. Abdulle AE
   2. de Koning ME
   3. van der Horn HJ
   4. Scheenen ME
   5. Roks G
   6. Hageman G
   7. Spikman JM
   8. van der Naalt J

   (2018) Early predictors for long-term functional outcome after mild traumatic brain injury in frail elderly patients

   The Journal of Head Trauma Rehabilitation 33:E59–E67.

   https://doi.org/10.1097/HTR.0000000000000368

   • PubMed
   • Google Scholar
2. 1. Afgan E
   2. Baker D
   3. Batut B
   4. van den Beek M
   5. Bouvier D
   6. Cech M
   7. Chilton J
   8. Clements D
   9. Coraor N
   10. Grüning BA
   11. Guerler A
   12. Hillman-Jackson J
   13. Hiltemann S
   14. Jalili V
   15. Rasche H
   16. Soranzo N
   17. Goecks J
   18. Taylor J
   19. Nekrutenko A
   20. Blankenberg D

   (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

   Nucleic Acids Research 46:W537–W544.

   https://doi.org/10.1093/nar/gky379

   • PubMed
   • Google Scholar
3. Software

   1. Andrews S

   (2010) FastQC: a quality control tool for high throughput sequence data, version GPL v3

   Babraham Bioinformatics.

   http://www.bioinformatics.babraham.ac.uk/projects/fastqc
4. 1. Azam S
   2. Haque ME
   3. Balakrishnan R
   4. Kim IS
   5. Choi DK

   (2021) The ageing brain: molecular and cellular basis of neurodegeneration

   Frontiers in Cell and Developmental Biology 9:683459.

   https://doi.org/10.3389/fcell.2021.683459

   • PubMed
   • Google Scholar
5. 1. Barekat A
   2. Gonzalez A
   3. Mauntz RE
   4. Kotzebue RW
   5. Molina B
   6. El-Mecharrafie N
   7. Conner CJ
   8. Garza S
   9. Melkani GC
   10. Joiner WJ
   11. Lipinski MM
   12. Finley KD
   13. Ratliff EP

   (2016) Using Drosophila as an integrated model to study mild repetitive traumatic brain injury

   Scientific Reports 6:25252.

   https://doi.org/10.1038/srep25252

   • PubMed
   • Google Scholar
6. 1. Behnke JA
   2. Ye C
   3. Moberg KH
   4. Zheng JQ

   (2021a) A protocol to detect neurodegeneration in Drosophila melanogaster whole-brain mounts using advanced microscopy

   STAR Protocols 2:100689.

   https://doi.org/10.1016/j.xpro.2021.100689

   • PubMed
   • Google Scholar
7. 1. Behnke JA
   2. Ye C
   3. Setty A
   4. Moberg KH
   5. Zheng JQ

   (2021b) Repetitive mild head trauma induces activity mediated lifelong brain deficits in a novel Drosophila model

   Scientific Reports 11:9738.

   https://doi.org/10.1038/s41598-021-89121-7

   • PubMed
   • Google Scholar
8. 1. Bertram L
   2. Tanzi RE

   (2005) The genetic epidemiology of neurodegenerative disease

   The Journal of Clinical Investigation 115:1449–1457.

   https://doi.org/10.1172/JCI24761

   • PubMed
   • Google Scholar
9. 1. Berz K
   2. Divine J
   3. Foss KB
   4. Heyl R
   5. Ford KR
   6. Myer GD

   (2013) Sex-specific differences in the severity of symptoms and recovery rate following sports-related concussion in young athletes

   The Physician and Sportsmedicine 41:58–63.

   https://doi.org/10.3810/psm.2013.05.2015

   • PubMed
   • Google Scholar
10. 1. Binggeli O
    2. Neyen C
    3. Poidevin M
    4. Lemaitre B

    (2014) Prophenoloxidase activation is required for survival to microbial infections in Drosophila

    PLOS Pathogens 10:e1004067.

    https://doi.org/10.1371/journal.ppat.1004067

    • PubMed
    • Google Scholar
11. 1. Bittencourt-Villalpando M
    2. van der Horn HJ
    3. Maurits NM
    4. van der Naalt J

    (2021) Disentangling the effects of age and mild traumatic brain injury on brain network connectivity: A resting state fMRI study

    NeuroImage. Clinical 29:102534.

    https://doi.org/10.1016/j.nicl.2020.102534

    • PubMed
    • Google Scholar
12. 1. Bolus H
    2. Crocker K
    3. Boekhoff-Falk G
    4. Chtarbanova S

    (2020) Modeling neurodegenerative disorders in Drosophila melanogaster

    International Journal of Molecular Sciences 21:3055.

    https://doi.org/10.3390/ijms21093055

    • PubMed
    • Google Scholar
13. 1. Brett BL
    2. Gardner RC
    3. Godbout J
    4. Dams-O’Connor K
    5. Keene CD

    (2022) Traumatic brain injury and risk of neurodegenerative disorder

    Biological Psychiatry 91:498–507.

    https://doi.org/10.1016/j.biopsych.2021.05.025

    • PubMed
    • Google Scholar
14. 1. Brown RC
    2. Lockwood AH
    3. Sonawane BR

    (2005) Neurodegenerative diseases: an overview of environmental risk factors

    Environmental Health Perspectives 113:1250–1256.

    https://doi.org/10.1289/ehp.7567

    • PubMed
    • Google Scholar
15. 1. Byrns CN
    2. Saikumar J
    3. Bonini NM

    (2021) Glial AP1 is activated with aging and accelerated by traumatic brain injury

    Nature Aging 1:585–597.

    https://doi.org/10.1038/s43587-021-00072-0

    • PubMed
    • Google Scholar
16. 1. Cerri S
    2. Mus L
    3. Blandini F

    (2019) Parkinson’s disease in women and men: What’s the difference?

    Journal of Parkinson’s Disease 9:501–515.

    https://doi.org/10.3233/JPD-191683

    • PubMed
    • Google Scholar
17. 1. Chapman T
    2. Bangham J
    3. Vinti G
    4. Seifried B
    5. Lung O
    6. Wolfner MF
    7. Smith HK
    8. Partridge L

    (2003) The sex peptide of Drosophila melanogaster: female post-mating responses analyzed by using RNA interference

    PNAS 100:9923–9928.

    https://doi.org/10.1073/pnas.1631635100

    • PubMed
    • Google Scholar
18. 1. Cheng G
    2. Kong R
    3. Zhang L
    4. Zhang J

    (2012) Mitochondria in traumatic brain injury and mitochondrial-targeted multipotential therapeutic strategies

    British Journal of Pharmacology 167:699–719.

    https://doi.org/10.1111/j.1476-5381.2012.02025.x

    • PubMed
    • Google Scholar
19. 1. Chistiakov DA
    2. Sobenin IA
    3. Revin VV
    4. Orekhov AN
    5. Bobryshev YV

    (2014) Mitochondrial aging and age-related dysfunction of mitochondria

    BioMed Research International 2014:238463.

    https://doi.org/10.1155/2014/238463

    • PubMed
    • Google Scholar
20. 1. Cui H
    2. Kong Y
    3. Zhang H

    (2012) Oxidative stress, mitochondrial dysfunction, and aging

    Journal of Signal Transduction 2012:646354.

    https://doi.org/10.1155/2012/646354

    • PubMed
    • Google Scholar
21. 1. Cunningham F
    2. Allen JE
    3. Allen J
    4. Alvarez-Jarreta J
    5. Amode MR
    6. Armean IM
    7. Austine-Orimoloye O
    8. Azov AG
    9. Barnes I
    10. Bennett R
    11. Berry A
    12. Bhai J
    13. Bignell A
    14. Billis K
    15. Boddu S
    16. Brooks L
    17. Charkhchi M
    18. Cummins C
    19. Da Rin Fioretto L
    20. Davidson C
    21. Dodiya K
    22. Donaldson S
    23. El Houdaigui B
    24. El Naboulsi T
    25. Fatima R
    26. Giron CG
    27. Genez T
    28. Martinez JG
    29. Guijarro-Clarke C
    30. Gymer A
    31. Hardy M
    32. Hollis Z
    33. Hourlier T
    34. Hunt T
    35. Juettemann T
    36. Kaikala V
    37. Kay M
    38. Lavidas I
    39. Le T
    40. Lemos D
    41. Marugán JC
    42. Mohanan S
    43. Mushtaq A
    44. Naven M
    45. Ogeh DN
    46. Parker A
    47. Parton A
    48. Perry M
    49. Piližota I
    50. Prosovetskaia I
    51. Sakthivel MP
    52. Salam AIA
    53. Schmitt BM
    54. Schuilenburg H
    55. Sheppard D
    56. Pérez-Silva JG
    57. Stark W
    58. Steed E
    59. Sutinen K
    60. Sukumaran R
    61. Sumathipala D
    62. Suner M-M
    63. Szpak M
    64. Thormann A
    65. Tricomi FF
    66. Urbina-Gómez D
    67. Veidenberg A
    68. Walsh TA
    69. Walts B
    70. Willhoft N
    71. Winterbottom A
    72. Wass E
    73. Chakiachvili M
    74. Flint B
    75. Frankish A
    76. Giorgetti S
    77. Haggerty L
    78. Hunt SE
    79. IIsley GR
    80. Loveland JE
    81. Martin FJ
    82. Moore B
    83. Mudge JM
    84. Muffato M
    85. Perry E
    86. Ruffier M
    87. Tate J
    88. Thybert D
    89. Trevanion SJ
    90. Dyer S
    91. Harrison PW
    92. Howe KL
    93. Yates AD
    94. Zerbino DR
    95. Flicek P

    (2022) Ensembl 2022

    Nucleic Acids Research 50:D988–D995.

    https://doi.org/10.1093/nar/gkab1049

    • Google Scholar
22. 1. Danecek P
    2. Bonfield JK
    3. Liddle J
    4. Marshall J
    5. Ohan V
    6. Pollard MO
    7. Whitwham A
    8. Keane T
    9. McCarthy SA
    10. Davies RM
    11. Li H

    (2021) Twelve years of SAMtools and BCFtools

    GigaScience 10:giab008.

    https://doi.org/10.1093/gigascience/giab008

    • PubMed
    • Google Scholar
23. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
24. 1. Doust YV
    2. Rowe RK
    3. Adelson PD
    4. Lifshitz J
    5. Ziebell JM

    (2021) Age-at-injury determines the extent of long-term neuropathology and microgliosis after a diffuse brain injury in male rats

    Frontiers in Neurology 12:722526.

    https://doi.org/10.3389/fneur.2021.722526

    • PubMed
    • Google Scholar
25. 1. Flatt T
    2. Heyland A
    3. Rus F
    4. Porpiglia E
    5. Sherlock C
    6. Yamamoto R
    7. Garbuzov A
    8. Palli SR
    9. Tatar M
    10. Silverman N

    (2008) Hormonal regulation of the humoral innate immune response in Drosophila melanogaster

    The Journal of Experimental Biology 211:2712–2724.

    https://doi.org/10.1242/jeb.014878

    • PubMed
    • Google Scholar
26. 1. Fu WY
    2. Ip NY

    (2023) The role of genetic risk factors of Alzheimer’s disease in synaptic dysfunction

    Seminars in Cell & Developmental Biology 139:3–12.

    https://doi.org/10.1016/j.semcdb.2022.07.011

    • PubMed
    • Google Scholar
27. 1. Garbe DS
    2. Vigderman AS
    3. Moscato E
    4. Dove AE
    5. Vecsey CG
    6. Kayser MS
    7. Sehgal A

    (2016) Changes in female Drosophila sleep following mating are mediated by SPSN-SAG neurons

    Journal of Biological Rhythms 31:551–567.

    https://doi.org/10.1177/0748730416668048

    • PubMed
    • Google Scholar
28. 1. Gilsanz P
    2. Lee C
    3. Corrada MM
    4. Kawas CH
    5. Quesenberry CP Jr
    6. Whitmer RA

    (2019) Reproductive period and risk of dementia in a diverse cohort of health care members

    Neurology 92:e2005–e2014.

    https://doi.org/10.1212/WNL.0000000000007326

    • PubMed
    • Google Scholar
29. 1. Gordon KE
    2. Wolfner MF
    3. Lazzaro BP

    (2022) A single mating is sufficient to induce persistent reduction of immune defense in mated female Drosophila melanogaster

    Journal of Insect Physiology 140:104414.

    https://doi.org/10.1016/j.jinsphys.2022.104414

    • PubMed
    • Google Scholar
30. 1. Gramates LS
    2. Agapite J
    3. Attrill H
    4. Calvi BR
    5. Crosby MA
    6. Dos Santos G
    7. Goodman JL
    8. Goutte-Gattat D
    9. Jenkins VK
    10. Kaufman T
    11. Larkin A
    12. Matthews BB
    13. Millburn G
    14. Strelets VB
    15. the FlyBase Consortium

    (2022) FlyBase: a guided tour of highlighted features

    Genetics 220:iyac035.

    https://doi.org/10.1093/genetics/iyac035

    • PubMed
    • Google Scholar
31. 1. Gupta R
    2. Sen N

    (2016) Traumatic brain injury: a risk factor for neurodegenerative diseases

    Reviews in the Neurosciences 27:93–100.

    https://doi.org/10.1515/revneuro-2015-0017

    • PubMed
    • Google Scholar
32. 1. Gupte R
    2. Brooks W
    3. Vukas R
    4. Pierce J
    5. Harris J

    (2019) Sex differences in traumatic brain injury: What we know and what we should know

    Journal of Neurotrauma 36:3063–3091.

    https://doi.org/10.1089/neu.2018.6171

    • PubMed
    • Google Scholar
33. 1. Ha DM
    2. Xu J
    3. Janowsky JS

    (2007) Preliminary evidence that long-term estrogen use reduces white matter loss in aging

    Neurobiology of Aging 28:1936–1940.

    https://doi.org/10.1016/j.neurobiolaging.2006.08.007

    • PubMed
    • Google Scholar
34. 1. Hanson MA
    2. Lemaitre B

    (2023) Antimicrobial peptides do not directly contribute to aging in Drosophila, but improve lifespan by preventing dysbiosis

    Disease Models & Mechanisms 16:dmm049965.

    https://doi.org/10.1242/dmm.049965

    • PubMed
    • Google Scholar
35. 1. Hardy J
    2. Orr H

    (2006) The genetics of neurodegenerative diseases

    Journal of Neurochemistry 97:1690–1699.

    https://doi.org/10.1111/j.1471-4159.2006.03979.x

    • PubMed
    • Google Scholar
36. 1. Häsemeyer M
    2. Yapici N
    3. Heberlein U
    4. Dickson BJ

    (2009) Sensory neurons in the Drosophila genital tract regulate female reproductive behavior

    Neuron 61:511–518.

    https://doi.org/10.1016/j.neuron.2009.01.009

    • PubMed
    • Google Scholar
37. 1. Haynes L

    (2020) Aging of the immune system: Research challenges to enhance the health span of older adults

    Frontiers in Aging 1:602108.

    https://doi.org/10.3389/fragi.2020.602108

    • PubMed
    • Google Scholar
38. 1. Heisenberg M
    2. Böhl K

    (1979) Isolation of anatomical brain mutants of Drosophila by histological means

    Zeitschrift Für Naturforschung C 34:143–147.

    https://doi.org/10.1515/znc-1979-1-228

    • Google Scholar
39. 1. Hirth F

    (2010) Drosophila melanogaster in the study of human neurodegeneration

    CNS & Neurological Disorders Drug Targets 9:504–523.

    https://doi.org/10.2174/187152710791556104

    • PubMed
    • Google Scholar
40. 1. Isaac RE
    2. Li C
    3. Leedale AE
    4. Shirras AD

    (2010) Drosophila male sex peptide inhibits siesta sleep and promotes locomotor activity in the post-mated female

    Proceedings. Biological Sciences 277:65–70.

    https://doi.org/10.1098/rspb.2009.1236

    • PubMed
    • Google Scholar
41. 1. Jacobs B
    2. Beems T
    3. Stulemeijer M
    4. van Vugt AB
    5. van der Vliet TM
    6. Borm GF
    7. Vos PE

    (2010) Outcome prediction in mild traumatic brain injury: age and clinical variables are stronger predictors than CT abnormalities

    Journal of Neurotrauma 27:655–668.

    https://doi.org/10.1089/neu.2009.1059

    • PubMed
    • Google Scholar
42. 1. Jang H
    2. Bae JB
    3. Dardiotis E
    4. Scarmeas N
    5. Sachdev PS
    6. Lipnicki DM
    7. Han JW
    8. Kim TH
    9. Kwak KP
    10. Kim BJ
    11. Kim SG
    12. Kim JL
    13. Moon SW
    14. Park JH
    15. Ryu S-H
    16. Youn JC
    17. Lee DY
    18. Lee DW
    19. Lee SB
    20. Lee JJ
    21. Jhoo JH
    22. Yannakoulia M
    23. Kosmidis MH
    24. Hadjigeorgiou GM
    25. Sakka P
    26. Kim KW

    (2018) Differential effects of completed and incomplete pregnancies on the risk of Alzheimer disease

    Neurology 91:e643–e651.

    https://doi.org/10.1212/WNL.0000000000006000

    • PubMed
    • Google Scholar
43. 1. Jones TB
    2. Mackey T
    3. Juba AN
    4. Amin K
    5. Atyam A
    6. McDole M
    7. Yancy J
    8. Thomas TC
    9. Buhlman LM

    (2024) Mild traumatic brain injury in Drosophila melanogaster alters reactive oxygen and nitrogen species in a sex-dependent manner

    Experimental Neurology 372:114621.

    https://doi.org/10.1016/j.expneurol.2023.114621

    • PubMed
    • Google Scholar
44. 1. Katzenberger RJ
    2. Loewen CA
    3. Wassarman DR
    4. Petersen AJ
    5. Ganetzky B
    6. Wassarman DA

    (2013) A Drosophila model of closed head traumatic brain injury

    PNAS 110:E4152–E4159.

    https://doi.org/10.1073/pnas.1316895110

    • PubMed
    • Google Scholar
45. 1. Katzenberger RJ
    2. Ganetzky B
    3. Wassarman DA

    (2016) Age and diet affect genetically separable secondary injuries that cause acute mortality following traumatic brain injury in Drosophila

    G3: Genes, Genomes, Genetics 6:4151–4166.

    https://doi.org/10.1534/g3.116.036194

    • PubMed
    • Google Scholar
46. 1. Kim S
    2. Han SC
    3. Gallan AJ
    4. Hayes JP

    (2017) Neurometabolic indicators of mitochondrial dysfunction in repetitive mild traumatic brain injury

    Concussion 2:CNC48.

    https://doi.org/10.2217/cnc-2017-0013

    • PubMed
    • Google Scholar
47. 1. Kubiak M
    2. Tinsley MC

    (2017) Sex-specific routes to immune senescence in Drosophila melanogaster

    Scientific Reports 7:10417.

    https://doi.org/10.1038/s41598-017-11021-6

    • PubMed
    • Google Scholar
48. 1. Lessing D
    2. Bonini NM

    (2009) Maintaining the brain: insight into human neurodegeneration from Drosophila melanogaster mutants

    Nature Reviews. Genetics 10:359–370.

    https://doi.org/10.1038/nrg2563

    • PubMed
    • Google Scholar
49. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

    Bioinformatics 30:923–930.

    https://doi.org/10.1093/bioinformatics/btt656

    • PubMed
    • Google Scholar
50. 1. Liu H
    2. Kubli E

    (2003) Sex-peptide is the molecular basis of the sperm effect in Drosophila melanogaster

    PNAS 100:9929–9933.

    https://doi.org/10.1073/pnas.1631700100

    • PubMed
    • Google Scholar
51. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
52. 1. McGurk L
    2. Berson A
    3. Bonini NM

    (2015) Drosophila as an in vivo model for human neurodegenerative disease

    Genetics 201:377–402.

    https://doi.org/10.1534/genetics.115.179457

    • PubMed
    • Google Scholar
53. 1. Moretti L
    2. Cristofori I
    3. Weaver SM
    4. Chau A
    5. Portelli JN
    6. Grafman J

    (2012) Cognitive decline in older adults with a history of traumatic brain injury

    The Lancet. Neurology 11:1103–1112.

    https://doi.org/10.1016/S1474-4422(12)70226-0

    • PubMed
    • Google Scholar
54. 1. Mosenthal AC
    2. Livingston DH
    3. Lavery RF
    4. Knudson MM
    5. Lee S
    6. Morabito D
    7. Manley GT
    8. Nathens A
    9. Jurkovich G
    10. Hoyt DB
    11. Coimbra R

    (2004) The Effect of age on functional outcome in mild traumatic brain injury: 6-month report of a prospective multicenter trial

    The Journal of Trauma 56:1042–1048.

    https://doi.org/10.1097/01.TA.0000127767.83267.33

    • Google Scholar
55. 1. Nebel RA
    2. Aggarwal NT
    3. Barnes LL
    4. Gallagher A
    5. Goldstein JM
    6. Kantarci K
    7. Mallampalli MP
    8. Mormino EC
    9. Scott L
    10. Yu WH
    11. Maki PM
    12. Mielke MM

    (2018) Understanding the impact of sex and gender in Alzheimer’s disease: A call to action

    Alzheimer’s & Dementia 14:1171–1183.

    https://doi.org/10.1016/j.jalz.2018.04.008

    • PubMed
    • Google Scholar
56. 1. Needham EJ
    2. Helmy A
    3. Zanier ER
    4. Jones JL
    5. Coles AJ
    6. Menon DK

    (2019) The immunological response to traumatic brain injury

    Journal of Neuroimmunology 332:112–125.

    https://doi.org/10.1016/j.jneuroim.2019.04.005

    • PubMed
    • Google Scholar
57. 1. Ntikas M
    2. Binkofski F
    3. Shah NJ
    4. Ietswaart M

    (2022) Repeated sub-concussive impacts and the negative effects of contact sports on cognition and brain integrity

    International Journal of Environmental Research and Public Health 19:7098.

    https://doi.org/10.3390/ijerph19127098

    • PubMed
    • Google Scholar
58. 1. Ojo JO
    2. Mouzon B
    3. Greenberg MB
    4. Bachmeier C
    5. Mullan M
    6. Crawford F

    (2013) Repetitive mild traumatic brain injury augments tau pathology and glial activation in aged hTau mice

    Journal of Neuropathology and Experimental Neurology 72:137–151.

    https://doi.org/10.1097/NEN.0b013e3182814cdf

    • PubMed
    • Google Scholar
59. 1. Pandey UB
    2. Nichols CD

    (2011) Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery

    Pharmacological Reviews 63:411–436.

    https://doi.org/10.1124/pr.110.003293

    • PubMed
    • Google Scholar
60. 1. Peng J
    2. Zipperlen P
    3. Kubli E

    (2005) Drosophila sex-peptide stimulates female innate immune system after mating via the toll and imd pathways

    Current Biology 15:1690–1694.

    https://doi.org/10.1016/j.cub.2005.08.048

    • PubMed
    • Google Scholar
61. 1. Petersen AJ
    2. Wassarman DA

    (2012) Drosophila innate immune response pathways moonlight in neurodegeneration

    Fly 6:169–172.

    https://doi.org/10.4161/fly.20999

    • PubMed
    • Google Scholar
62. Report

    1. Peterson AB
    2. Xu L
    3. Daugherty J
    4. Breiding MJ

    (2014)

    Surveillance report of traumatic brain injury-related emergency department visits, hospitalizations, and deaths, United States

    Surveillance Report.

    • Google Scholar
63. Book

    1. Pihlstrøm L
    2. Wiethoff S
    3. Houlden H

    (2017) Genetics of neurodegenerative diseases: an overview

    In: Pihlstrøm L, editors. Handbook of Clinical Neurology. Elsevier. pp. 309–323.

    https://doi.org/10.1016/B978-0-12-802395-2.00022-5

    • Google Scholar
64. 1. Podcasy JL
    2. Epperson CN

    (2016) Considering sex and gender in Alzheimer disease and other dementias

    Dialogues in Clinical Neuroscience 18:437–446.

    https://doi.org/10.31887/DCNS.2016.18.4/cepperson

    • PubMed
    • Google Scholar
65. 1. Prüßing K
    2. Voigt A
    3. Schulz JB

    (2013) Drosophila melanogaster as a model organism for Alzheimer’s disease

    Molecular Neurodegeneration 8:35.

    https://doi.org/10.1186/1750-1326-8-35

    • PubMed
    • Google Scholar
66. 1. Rauen K
    2. Späni CB
    3. Tartaglia MC
    4. Ferretti MT
    5. Reichelt L
    6. Probst P
    7. Schäpers B
    8. Müller F
    9. Jahn K
    10. Plesnila N

    (2021) Quality of life after traumatic brain injury: a cross-sectional analysis uncovers age- and sex-related differences over the adult life span

    GeroScience 43:263–278.

    https://doi.org/10.1007/s11357-020-00273-2

    • PubMed
    • Google Scholar
67. 1. Regan JC
    2. Khericha M
    3. Dobson AJ
    4. Bolukbasi E
    5. Rattanavirotkul N
    6. Partridge L

    (2016) Sex difference in pathology of the ageing gut mediates the greater response of female lifespan to dietary restriction

    eLife 5:e10956.

    https://doi.org/10.7554/eLife.10956

    • PubMed
    • Google Scholar
68. 1. Rezával C
    2. Pavlou HJ
    3. Dornan AJ
    4. Chan Y-B
    5. Kravitz EA
    6. Goodwin SF

    (2012) Neural circuitry underlying Drosophila female postmating behavioral responses

    Current Biology 22:1155–1165.

    https://doi.org/10.1016/j.cub.2012.04.062

    • PubMed
    • Google Scholar
69. 1. Robinson JT
    2. Thorvaldsdóttir H
    3. Winckler W
    4. Guttman M
    5. Lander ES
    6. Getz G
    7. Mesirov JP

    (2011) Integrative genomics viewer

    Nature Biotechnology 29:24–26.

    https://doi.org/10.1038/nbt.1754

    • PubMed
    • Google Scholar
70. 1. Romero-Ferrero F
    2. Bergomi MG
    3. Hinz RC
    4. Heras FJH
    5. de Polavieja GG

    (2019) idtracker.ai: tracking all individuals in small or large collectives of unmarked animals

    Nature Methods 16:179–182.

    https://doi.org/10.1038/s41592-018-0295-5

    • PubMed
    • Google Scholar
71. 1. Rowe RK
    2. Ziebell JM
    3. Harrison JL
    4. Law LM
    5. Adelson PD
    6. Lifshitz J

    (2016) Aging with traumatic brain injury: Effects of age at injury on behavioral outcome following diffuse brain injury in rats

    Developmental Neuroscience 38:195–205.

    https://doi.org/10.1159/000446773

    • PubMed
    • Google Scholar
72. 1. Saikumar J
    2. Byrns CN
    3. Hemphill M
    4. Meaney DF
    5. Bonini NM

    (2020) Dynamic neural and glial responses of a head-specific model for traumatic brain injury in Drosophila

    PNAS 117:17269–17277.

    https://doi.org/10.1073/pnas.2003909117

    • Google Scholar
73. 1. Saikumar J
    2. Bonini NM

    (2021) Synergistic effects of brain injury and aging: common mechanisms of proteostatic dysfunction

    Trends in Neurosciences 44:728–740.

    https://doi.org/10.1016/j.tins.2021.06.003

    • PubMed
    • Google Scholar
74. 1. Sanuki R

    (2020) Drosophila models of traumatic brain injury

    Frontiers in Bioscience 25:168–178.

    https://doi.org/10.2741/4801

    • Google Scholar
75. 1. Schwenke RA
    2. Lazzaro BP

    (2017) Juvenile hormone suppresses resistance to infection in mated female Drosophila melanogaster

    Current Biology 27:596–601.

    https://doi.org/10.1016/j.cub.2017.01.004

    • PubMed
    • Google Scholar
76. 1. Short SM
    2. Lazzaro BP

    (2010) Female and male genetic contributions to post-mating immune defence in female Drosophila melanogaster

    Proceedings. Biological Sciences 277:3649–3657.

    https://doi.org/10.1098/rspb.2010.0937

    • PubMed
    • Google Scholar
77. 1. Short SM
    2. Wolfner MF
    3. Lazzaro BP

    (2012) Female Drosophila melanogaster suffer reduced defense against infection due to seminal fluid components

    Journal of Insect Physiology 58:1192–1201.

    https://doi.org/10.1016/j.jinsphys.2012.06.002

    • PubMed
    • Google Scholar
78. 1. Song YJ
    2. Li SR
    3. Li XW
    4. Chen X
    5. Wei ZX
    6. Liu QS
    7. Cheng Y

    (2020) The effect of estrogen replacement therapy on Alzheimer’s disease and Parkinson’s disease in postmenopausal women: A meta-analysis

    Frontiers in Neuroscience 14:157.

    https://doi.org/10.3389/fnins.2020.00157

    • PubMed
    • Google Scholar
79. 1. Sun M
    2. Chen LL

    (2017) A novel method to model chronic traumatic encephalopathy in Drosophila

    Journal of Visualized Experiments 01:55602.

    https://doi.org/10.3791/55602

    • PubMed
    • Google Scholar
80. 1. Sunderhaus ER
    2. Kretzschmar D

    (2016) Mass histology to quantify neurodegeneration in Drosophila

    Journal of Visualized Experiments 01:54809.

    https://doi.org/10.3791/54809

    • PubMed
    • Google Scholar
81. 1. van Alphen B
    2. Stewart S
    3. Iwanaszko M
    4. Xu F
    5. Li K
    6. Rozenfeld S
    7. Ramakrishnan A
    8. Itoh TQ
    9. Sisobhan S
    10. Qin Z
    11. Lear BC
    12. Allada R

    (2022) Glial immune-related pathways mediate effects of closed head traumatic brain injury on behavior and lethality in Drosophila

    PLOS Biology 20:e3001456.

    https://doi.org/10.1371/journal.pbio.3001456

    • PubMed
    • Google Scholar
82. 1. Vegeto E
    2. Villa A
    3. Della Torre S
    4. Crippa V
    5. Rusmini P
    6. Cristofani R
    7. Galbiati M
    8. Maggi A
    9. Poletti A

    (2020) The role of sex and sex hormones in neurodegenerative diseases

    Endocrine Reviews 41:273–319.

    https://doi.org/10.1210/endrev/bnz005

    • PubMed
    • Google Scholar
83. 1. Verboon LN
    2. Patel HC
    3. Greenhalgh AD

    (2021) The immune system’s role in the consequences of mild traumatic brain injury (concussion)

    Frontiers in Immunology 12:620698.

    https://doi.org/10.3389/fimmu.2021.620698

    • PubMed
    • Google Scholar
84. 1. Wang L
    2. Wang S
    3. Li W

    (2012) RSeQC: quality control of RNA-seq experiments

    Bioinformatics 28:2184–2185.

    https://doi.org/10.1093/bioinformatics/bts356

    • PubMed
    • Google Scholar
85. 1. Wang F
    2. Wang K
    3. Forknall N
    4. Patrick C
    5. Yang T
    6. Parekh R
    7. Bock D
    8. Dickson BJ

    (2020) Neural circuitry linking mating and egg laying in Drosophila females

    Nature 579:101–105.

    https://doi.org/10.1038/s41586-020-2055-9

    • Google Scholar
86. 1. Weyand CM
    2. Goronzy JJ

    (2016) Aging of the immune system: mechanisms and therapeutic targets

    Annals of the American Thoracic Society 13 Suppl 5:S422–S428.

    https://doi.org/10.1513/AnnalsATS.201602-095AW

    • PubMed
    • Google Scholar
87. 1. Wu T
    2. Hu E
    3. Xu S
    4. Chen M
    5. Guo P
    6. Dai Z
    7. Feng T
    8. Zhou L
    9. Tang W
    10. Zhan L
    11. Fu X
    12. Liu S
    13. Bo X
    14. Yu G

    (2021) clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

    Innovation 2:100141.

    https://doi.org/10.1016/j.xinn.2021.100141

    • PubMed
    • Google Scholar
88. 1. Xiong J
    2. Kang SS
    3. Wang Z
    4. Liu X
    5. Kuo T-C
    6. Korkmaz F
    7. Padilla A
    8. Miyashita S
    9. Chan P
    10. Zhang Z
    11. Katsel P
    12. Burgess J
    13. Gumerova A
    14. Ievleva K
    15. Sant D
    16. Yu S-P
    17. Muradova V
    18. Frolinger T
    19. Lizneva D
    20. Iqbal J
    21. Goosens KA
    22. Gera S
    23. Rosen CJ
    24. Haroutunian V
    25. Ryu V
    26. Yuen T
    27. Zaidi M
    28. Ye K

    (2022) FSH blockade improves cognition in mice with Alzheimer’s disease

    Nature 603:470–476.

    https://doi.org/10.1038/s41586-022-04463-0

    • PubMed
    • Google Scholar
89. 1. Yang CH
    2. Rumpf S
    3. Xiang Y
    4. Gordon MD
    5. Song W
    6. Jan LY
    7. Jan YN

    (2009) Control of the postmating behavioral switch in Drosophila females by internal sensory neurons

    Neuron 61:519–526.

    https://doi.org/10.1016/j.neuron.2008.12.021

    • PubMed
    • Google Scholar
90. 1. Yapici N
    2. Kim YJ
    3. Ribeiro C
    4. Dickson BJ

    (2008) A receptor that mediates the post-mating switch in Drosophila reproductive behaviour

    Nature 451:33–37.

    https://doi.org/10.1038/nature06483

    • PubMed
    • Google Scholar
91. 1. Ye C
    2. Behnke JA
    3. Hardin KR
    4. Zheng JQ

    (2023) Drosophila melanogaster as a model to study age and sex differences in brain injury and neurodegeneration after mild head trauma

    Frontiers in Neuroscience 17:1150694.

    https://doi.org/10.3389/fnins.2023.1150694

    • PubMed
    • Google Scholar
92. 1. Yue JK
    2. Levin HS
    3. Suen CG
    4. Morrissey MR
    5. Runyon SJ
    6. Winkler EA
    7. Puffer RC
    8. Deng H
    9. Robinson CK
    10. Rick JW
    11. Phelps RRL
    12. Sharma S
    13. Taylor SR
    14. Vassar MJ
    15. Cnossen MC
    16. Lingsma HF
    17. Gardner RC
    18. Temkin NR
    19. Barber J
    20. Dikmen SS
    21. Yuh EL
    22. Mukherjee P
    23. Stein MB
    24. Cage TA
    25. Valadka AB
    26. Okonkwo DO
    27. Manley GT
    28. TRACK-TBI Investigators

    (2019) Age and sex-mediated differences in six-month outcomes after mild traumatic brain injury in young adults: a TRACK-TBI study

    Neurological Research 41:609–623.

    https://doi.org/10.1080/01616412.2019.1602312

    • PubMed
    • Google Scholar
93. 1. Yuva-Aydemir Y
    2. Almeida S
    3. Gao FB

    (2018) Insights into C9ORF72-related ALS/FTD from Drosophila and iPSC models

    Trends in Neurosciences 41:457–469.

    https://doi.org/10.1016/j.tins.2018.04.002

    • PubMed
    • Google Scholar

Author details

1. Changtian Ye

   Department of Cell Biology, Emory University School of Medicine, Atlanta, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5381-410X

2. Ryan Ho

   College of Art and Science, Emory University, Atlanta, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

3. Kenneth H Moberg

   Department of Cell Biology, Emory University School of Medicine, Atlanta, United States

   Contribution

   Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9820-5543

4. James Q Zheng

   1. Department of Cell Biology, Emory University School of Medicine, Atlanta, United States
   2. Department of Neurology, Emory University School of Medicine, Atlanta, United States
   3. Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Project administration, Writing – review and editing

   For correspondence

   james.zheng@emory.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8093-422X

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