Biology subjects

Ma, H.

Publications and source records attributed to Ma, H..

At least 163 records · Page 9Linked to original sources

bioRxiv2022

Enhancement of central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting the beta-1 adrenergic receptor (β1-AR) locating in the cardiomyocytes in the DBA/1 mouse SUDEP model

Sudden unexpected death of epilepsy (SUDEP) is the leading cause of of death in patients with epilepsy. Due to the complicated pathogenesis of SUDEP, however, the exact mechanism of SUDEP remains elusive. Currently, although it is recognized that the seizure-induced respiratory arrest (S-IRA) may be a main cause for SUDEP, other factors resulting in SUDEP can not be excluded e.g arrhythmias. Our previous findings indicated that the incidence of S-IRA and SUDEP evoked by acoustic stimulation or pentetrazol (PTZ) injection was significantly reduced by atomoxetine, a norepinephrine reuptake inhibitor (NRI), suggesting that noradrenergic neurotransmission modulates S-IRA and SUDEP. Given that norepinephrine acts on the target to modulate respiratory and circulation function by targeting adrenergic receptor and beta (a-AR and {beta}-AR) and the arrhythmias can be contributed to SUDEP. Meanwhile, to further test whether cardiac factors are implicated in S-IRA and SUDEP, we choose esmolol hydrochloride, a selective antagonist of {beta}1-AR to test it in our models. Our findings demonstrated that the lower incidence of S-IRA and SUDEP evoked by acoustic stimulation or PTZ injection in DBA/1 mice by administration with atomoxetine was significantly reversed by intraperitoneal (IP) of esmolol hydrochloride. Importantly, the data of electrocardiogram (ECG) showed that the cardiac arrhythmia including the ventricular tachycardia, ventricular premature beat and atrioventricular block can be evoked by acoustic stimulation or PTZ injection in our model. Administration of atomoxetine significantly reduced these arrhythmias and the incidence of S-IRA and SUDEP in our models. However, administration of esmolol hydrochloride with the dose without affecting ECG and mortality changing of DBA/1 significantly blocking the protective effects of atomoxetine on S-IRA and SUDEP in our models. Thus, the dysfunction of respiratory and circulation may be implicated in the pathogenesis of S-IRA and SUDEP. Enhancing the central norepinephrinergic neurotransmission in the brain contributes to inhibition of seizure-induced respiratory arrest by targeting {beta}1-AR locating in the cardiomyocytes. Furthermore, the suppression effects of S-IRA by atomoxetine was significantly reversed by the norepinephrine neuronal degradation in the LC in our models. Furthermore, PTZ-induced Tyrosine hydroxylase (TH), the rate-limiting enzyme in the synthesis of norepinephrine, activity but not TH content from the serum of left ventricle and the whole heart tissue was reduced following the S-IRA. Our findings will show a new light on decoding the pathogenesis of SUDEP concerning the pathway between the LC and heart.

neuroscience↗

bioRxiv2021

Substrate Specificity for Human Histidine Methyltransferase SETD3

Histidine methyltransferase SETD3 plays an important role in human biology and diseases. Previously, we showed that SETD3 catalyzes N3-methylation of histidine 73 in {beta}-actin (Kwiatkowski et al., 2018). Here we report integrated synthetic, biocatalytic, biostructural and computational analyses on human SETD3-catalyzed methylation of {beta}-actin peptides possessing histidine and its structurally and chemically diverse mimics. Our enzyme assays supported by biostructural analyses demonstrate that SETD3 has a broader substrate scope beyond histidine, including N-nucleophiles on the aromatic and aliphatic side chains. Quantum mechanical/molecular mechanical (QM/MM) molecular dynamics and free-energy simulations provide insight into binding geometries and the free energy barrier for the enzymatic methyl transfer to histidine mimics, further supporting experimental data that histidine is the superior SETD3 substrate over its analogs. This work demonstrates that human SETD3 has a potential to catalyze efficient methylation of several histidine mimics, overall providing mechanistic, biocatalytic and functional insight into {beta}-actin histidine methylation by SETD3.

biochemistry↗

bioRxiv2021

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM Reference FormatAbigail Dommer1{dagger}, Lorenzo Casalino1{dagger}, Fiona Kearns1{dagger}, Mia Rosenfeld1, Nicholas Wauer1, Surl-Hee Ahn1, John Russo,2 Sofia Oliveira3, Clare Morris1, AnthonyBogetti4, AndaTrifan5,6, Alexander Brace5,7, TerraSztain1,8, Austin Clyde5,7, Heng Ma5, Chakra Chennubhotla4, Hyungro Lee9, Matteo Turilli9, Syma Khalid10, Teresa Tamayo-Mendoza11, Matthew Welborn11, Anders Christensen11, Daniel G. A. Smith11, Zhuoran Qiao12, Sai Krishna Sirumalla11, Michael OConnor11, Frederick Manby11, Anima Anandkumar12,13, David Hardy6, James Phillips6, Abraham Stern13, Josh Romero13, David Clark13, Mitchell Dorrell14, Tom Maiden14, Lei Huang15, John McCalpin15, Christo- pherWoods3, Alan Gray13, MattWilliams3, Bryan Barker16, HarindaRajapaksha16, Richard Pitts16, Tom Gibbs13, John Stone6, Daniel Zuckerman2*, Adrian Mulholland3*, Thomas MillerIII11,12*, ShantenuJha9*, Arvind Ramanathan5*, Lillian Chong4*, Rommie Amaro1*. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing 21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI

biophysics↗

bioRxiv2021

Deep Learning-based Automated Rare Sperm Identification from Testes Biopsies

Non-obstructive azoospermia (NOA), the most severe form of male infertility, is currently treated using microsurgical sperm extraction (microTESE) to retrieve sperm cells for in vitro fertilization via intracytoplasmic sperm injection (IVF-ICSI). The success rate of this procedure for NOA patients is currently limited by the ability of andrologists to identify a few rare sperm cells among millions of background testis cells. To improve this success rate, we developed a convolution neural network (CNN) to detect rare sperm from low-resolution microscopy images of microTESE samples. Our CNN uses the U-Net architecture to perform pixel-based classification on image patches from brightfield microscopy, which is followed by morphological analysis to detect individual sperm instances. This CNN is trained using microscopy images of fluorescently labeled sperm, which is fixed to eliminate their motility, and doped into testis biopsies obtained from NOA patients. We initially tested this algorithm using purified sperm samples at different imaging magnifications in order to determine the upper bounds of performance. We then tested this algorithm by doping rare sperm cells into testis biopsy samples from NOA patients and found a sperm detection F1 score of 85.2%. These results demonstrate the potential to use automated microscopy to dramatically increase the amount of testis biopsy tissue that could be comprehensively examined, which greatly increases the chance of finding rare viable sperm, and thereby increases the success rates of IVF-ICSI for couples with NOA.

bioengineering↗

bioRxiv2021

Fast anther dehiscence state recognition system establishing by deep learning to screen heat tolerant cotton

Cotton is one of the most economically important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. The anther dehiscence or indehiscence directly determine the probability of fertilization in cotton. Thus, the rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. The single-stage model based on YOLOv5 has higher recognition efficiency and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies of Faster R-CNN model were proposed, the improved model has higher detection accuracy than YOLOv5 model. In addition, the percentage of dehiscent anther of randomly selected 30 cotton varieties were observed from cotton population under normal temperature and high temperature (HT) conditions through the integrated Faster R-CNN model and manual observation. The result showed HT varying decreased the percentage of dehiscent anther in different cotton lines, consistent with the manual method. Thus, this system can help us to rapid and accurate identification of HT-tolerant cotton. One sentence summaryThe deep learning technique was applied to identify the anther dehiscence state for the first time to quickly screen heat tolerant cotton varieties and help to explore key genetic improvement genes.

bioengineering↗

bioRxiv2021

Emergence of an adaptive epigenetic cell state in human bladder urothelial carcinoma evolution

Intratumor heterogeneity (ITH) of bladder cancer (BLCA) facilitates therapy resistance and immune evasion to affect clinical prognosis directly. However, the molecular and cellular mechanism generating ITH in BLCA remains elusive. Here we show that a TM4SF1-positive cancer subpopulation (TPCS) drives ITH diversification in BLCA. By extensive profiling of the epigenome and transcriptome of BLCA from 79 donors across all stages, we elucidated the evolution trajectories of luminal and basal BLCA. TPCS emerges from the basal trajectory and shows extensive transcriptional plasticity with a distinct epigenomic landscape. Clinically, TPCS were enriched in advanced stage patients and associated with poor prognosis. Our results showed how cancer adapts to its environment by adopting a stem cell-like epigenomic landscape.

cancer biology↗

bioRxiv2021

Phylogenomic analyses in the apple genus Malus s.l. reveal widespread hybridization and allopolyploidy driving the diversifications, with insights into the complex biogeographic history in the Northern Hemisphere

Phylogenomic evidence from an increasing number of studies has demonstrated that different data sets and analytical approaches often reconstruct strongly supported but conflicting relationships. In this study, hundreds of single-copy nuclear (SCN) genes (785) and complete plastomes (75) were used to infer the phylogenetic relationships and estimate the historical biogeography of the apple genus Malus sensu lato, an economically important lineage disjunctly distributed in the Northern Hemisphere involved in known and suspected hybridization and allopolyploidy events. The nuclear phylogeny recovered the monophyly of Malus s.l. (including Docynia); however, it was supported to be biphyletic in the plastid phylogeny. An ancient chloroplast capture event best explains the cytonuclear discordance that occurred in the Eocene in western North America. Our conflict analysis demonstrated that ILS, hybridization, and allopolyploidy could explain the widespread nuclear gene tree discordance. We detected one deep hybridization event (Malus doumeri) involving the ancestor of pome-bearing species and Docynia delavayi, and one recent hybridization event (Malus coronaria) between M. sieversii and a combined clade of M. ioensis and M. angustifolia. Furthermore, our historical biogeographic analysis combining living and fossil species supported a widespread East Asian-western North American origin of Malus s.l., followed by a series of extinction events in the Eocene in northern East Aisa and western North America. This study provides a valuable evolutionary framework for the breeding and crop improvement of apples and their close relatives.

evolutionary biology↗

bioRxiv2021

Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

biophysics↗

bioRxiv2021

Broad ultra-potent neutralization of SARS-CoV-2 variants by monoclonal antibodies specific to the tip of RBD

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) continue to wreak havoc across the globe. Higher transmissibility and immunologic resistance of VOCs bring unprecedented challenges to epidemic extinguishment. Here we describe a monoclonal antibody, 2G1, that neutralizes all current VOCs and has surprising tolerance to mutations adjacent to or within its interaction epitope. Cryo-electron microscopy structure showed that 2G1 bound to the tip of receptor binding domain (RBD) of spike protein with small contact interface but strong hydrophobic effect, which resulted in nanomolar to sub-nanomolar affinities to spike proteins. The epitope of 2G1 on RBD partially overlaps with ACE2 interface, which gives 2G1 ability to block interaction between RBD and ACE2. The narrow binding epitope but high affinity bestow outstanding therapeutic efficacy upon 2G1 that neutralized VOCs with sub-nanomolar IC50 in vitro. In SARS-CoV-2 and Beta- and Delta-variant-challenged transgenic mice and rhesus macaque models, 2G1 protected animals from clinical illness and eliminated viral burden, without serious impact to animal safety. Mutagenesis experiments suggest that 2G1 could be potentially capable of dealing with emerging SARS-CoV-2 variants in future. This report characterized the therapeutic antibodies specific to the tip of spike against SARS-CoV-2 variants and highlights the potential clinical applications as well as for developing vaccine and cocktail therapy.

cell biology↗

bioRxiv2021

RNANetMotif: identifying sequence-structure RNA network motifs in RNA-protein binding sites

RNA molecules can adopt stable secondary and tertiary structures, which is essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via discrete molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs. Author SummaryRNA binding proteins (RBPs) regulate every aspect of RNA biology, including splicing, translation, transportation, and degradation. High-throughput technologies such as eCLIP have identified thousands of binding sites for a given RBP throughout the genome. It has been shown by earlier studies that, in addition to nucleotide sequences, the structure and conformation of RNAs also play important role in RBP-RNA interactions. Analogous to protein-protein interactions or protein-DNA interactions, it is likely that there exist intrinsic sequence-structure motifs common to these RNAs that underlie their binding specificity to specific RBPs. It is known that RNAs form energetically favorable secondary structures, which can be represented as a graph, with nucleotides being nodes and backbone covalent bonds and base-pairing hydrogen bonds representing edges. We hypothesize that these graphs can be mined by graph theory approaches to identify sequence-structure motifs as enriched sub-graphs. In this article, we described the details of this approach, termed RNANetMotif and associated new concepts, namely EKS (Extended K-mer Subgraphs) and GraphK graph search algorithm. To test the utility of our approach, we conducted 3D structure modeling of selected RNA sequences through molecular dynamics (MD) folding simulation and evaluated the significance of the discovered RNA motifs by comparing their spatial exposure with other regions on the RNA. We believe that this approach has the novelty of treating the RNA sequence as a graph and RBP binding sites as enriched subgraph, which has broader applications beyond RBP-RNA interactions.

bioinformatics↗

bioRxiv2021

Global trade-offs in tree functional traits

AO_SCPLOWBSTRACTC_SCPLOWDue to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. When considering trait relationships across the entire plant kingdom, plant trait frameworks typically must omit traits unique to large woody species, thereby limiting our understanding of how these distinct ecological pressures shape trait relationships in trees. Here, by considering 18 functional traits--reflecting leaf economics, wood structure, tree size, reproduction, and below-ground allocation--we quantify the major axes of variation governing trait expression of trees worldwide. We show that trait variation within and across angiosperms and gymnosperms is captured by two independent processes: one reflecting tree size and competition for light, the other reflecting leaf photosynthetic capacity and nutrient economies. By exploring multidimensional relationships across clusters of traits, we further identify a representative set of seven traits which captures the majority of variation in form and function in trees: maximum tree height, stem conduit diameter, specific leaf area, seed mass, bark thickness, root depth, and wood density. Collectively, this work informs future trait-based research into the functional biogeography of trees, and contributes to our fundamental understanding of the ecological and evolutionary controls on forest biodiversity and productivity worldwide.

ecology↗

bioRxiv2021

The Lyme Disease agent co-opts adiponectin receptor-mediated signaling in its arthropod vector

Adiponectin-mediated pathways contribute to mammalian homeostasis; however, little is known about adiponectin and adiponectin receptor signaling in arthropods. In this study, we demonstrate that Ixodes scapularis ticks have an adiponectin receptor-like protein (ISARL) but lack adiponectin - suggesting activation by alternative pathways. ISARL expression is significantly upregulated in the tick gut after Borrelia burgdorferi infection suggesting that ISARL-signaling may be co-opted by the Lyme disease agent. Consistent with this, RNA interference (RNAi)-mediated silencing of ISARL significantly reduced the B. burgdorferi burden in the tick. RNA-seq-based transcriptomics and RNAi assays demonstrate that ISARL-mediated phospholipid metabolism by phosphatidylserine synthase I is associated with B. burgdorferi survival. Furthermore, the tick complement C1q-like protein 3 interacts with ISARL, and B. burgdorferi facilitates this process. This study identifies a new tick metabolic pathway that is connected to the life cycle of the Lyme disease spirochete. Significance StatementAdiponectin binds to adiponectin receptors and participates in glucose and lipid metabolism in mammals. In this study, we found that ticks have an adiponectin receptor-like protein but lack adiponectin. Importantly, we demonstrated that the Lyme disease agent, Borrelia burgdorferi, takes advantage of the adiponectin receptor signaling pathway to establish infection in its arthropod vector, Ixodes scapularis. Our study sheds light on the understanding of Borrelia-tick interactions and provides insights into how a human infectious disease agent may evolve to manipulate host metabolism for its own benefits. Understanding this pathway may lead to new ways to interfere with the Borrelia life cycle, and this mechanism may be applicable to additional microbes that are transmitted by ticks, mosquitoes or other arthropods.

microbiology↗

bioRxiv2021

Long-term persistence of viral RNA and inflammation in the CNS of macaques exposed to aerosolized Venezuelan equine encephalitis virus.

Venezuelan equine encephalitis virus (VEEV) is a positively-stranded RNA arbovirus of the genus Alphavirus that causes encephalitis in humans. Cynomolgus macaques are a relevant model of the human disease caused by VEEV and are useful in exploring pathogenic mechanisms and the host response to VEEV infection. Macaques were exposed to small-particle aerosols containing virus derived from an infectious clone of VEEV strain INH-9813, a subtype IC strain isolated from a human infection. VEEV-exposed macaques developed a biphasic fever after infection similar to that seen in humans. Maximum temperature deviation correlated with the inhaled dose, but fever duration did not. Neurological signs, suggestive of virus penetration into the CNS, were predominantly seen in the second febrile period. Electroencephalography data indicated a statistically significant decrease in all power bands and circadian index during the second febrile period that returned to normal after fever resolved. Intracranial pressure increased late in the second febrile period. On day 6 post-infection macaques had high levels of MCP-1 and IP-10 chemokines in the CNS, as well as a marked increase of T lymphocytes and activated microglia. More than four weeks after infection, viral genomic RNA was found in the brain, cerebrospinal fluid and cervical lymph nodes. Pro-inflammatory cytokines & chemokines, infiltrating leukocytes and pathological changes were seen in the CNS tissues of macaques euthanized at these times. These data are consistent with persistence of virus replication and/or genomic RNA and potentially, inflammatory sequelae in the central nervous system after resolution of acute VEEV disease.

immunology↗

bioRxiv2021

Synaptic and mitochondrial plasticity associated with fear memory revealed by deep learning-based 3D reconstruction

Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a Region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we found that fear conditioning significantly increases the number of mitochondria but decreases their size, and promotes the formation of multi-contact synapses comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage capacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.

neuroscience↗

bioRxiv2021

Deep Learning Image Classification of Red Blood Cell Deformability

Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of these cells. A variety of methods have been developed to measure RBC deformability, but these methods require specialized equipment, long measurement time, and highly skilled personnel. To address this challenge, we investigated whether a machine learning approach could be applied to determine donor RBC deformability using single cell microscope images. We used the microfluidic ratchet device to sort RBCs based on deformability. Sorted cells are then imaged and used to train a deep learning model to classify RBCs based on deformability. This model correctly predicted deformability of individual RBCs with 84 {+/-} 11% accuracy averaged across ten donors. Using this model to score the deformability of RBC samples were accurate to within 4.4 {+/-} 2.5% of the value obtained using the microfluidic ratchet device. While machine learning methods are frequently developed to automate human image analysis, our study is remarkable in showing that deep learning of single cell microscopy images could be used to measure RBC deformability, a property not normally measurable by imaging. Measuring RBC deformability by imaging is also desirable because it can be performed rapidly using a standard microscopy system, potentially enabling RBC deformability studies to be performed as part of routine clinical assessments.

bioengineering↗

bioRxiv2021

The northern gene flow into southeastern East Asians inferred from genome-wide array genotyping

The population history of Southeast China remains poorly understood due to the sparse sampling of present-day populations and far less modeling with ancient genomic data. We here newly reported genome-wide genotyping data from 207 present-day Han Chinese and Hmong-Mien-speaking She people from Fujian and Taiwan, southeast China. We co-analyzed with 66 early-Neolithic to Iron-Age ancient Fujian and Taiwan individuals obtained from literature to explore the genetic continuity and admixture based on the genetic variations of high-resolution time transect. We found the genetic differentiation between northern and southern East Asians defined by a north-south East Asian genetic cline and the studied southern East Asians were clustered in the southern end of this cline. We also found that southeastern coastal continental modern East Asians harbored the genetic differentiation with other southern Tai-Kadai, Hmong-Mien, Austronesian and Austroasiatic speakers, as well as geographically close Neolithic-to-Iron Age populations, but relatedly close to post-Neolithic Yellow River ancients, which suggested the influence of southward gene flow on the modern southern coastal gene pool. Besides, we also identified one new Hmong-Mien genetic cline in East Asia with the coastal Fujian She localizing at the intersection position between Hmong-Mien and Han clines in the principal component analysis. She people show stronger genetic affinity with southern East Asian indigenous populations with the main ancestry deriving from Hanben-related populations. The southeastern Han Chinese could be modeled with the primary ancestry deriving from the group related to the Yellow River Basin millet farmers and the remaining from groups related to southeastern ancient indigenous rice farmers, which was consistent with the northern China origin of modern southeastern Han Chinese and in line with the historically and archaeologically attested southward migrations of Han people and their ancestors. Interestingly, f4-statistics and three-way admixture model results showed both coastal ancient sources related to Austronesian speakers and inland ancient sources related to Austroasiatic speakers complexed the modern observed fine-scale genetic structure here. Our estimated north-south admixture time ranges based on the decay of the linkage disequilibrium spanned from the Bronze age to historic periods, suggesting the recent large-scale population migrations and subsequent admixture participated in the formation of modern Han in Southeast Asia.

genetics↗

bioRxiv2021

Degradation of Red Blood Cell Deformability during Cold Storage in Blood Bags

Red blood cells (RBCs) stored in blood bags develop a storage lesion that include structural, metabolic, and morphologic transformations resulting in a progressive loss of RBC deformability. The speed of RBC deformability loss is donor-dependent, which if properly characterized, could be used as a biomarker to select high-quality RBC units for sensitive recipients or to provide customized storage timelines depending on the donor. We used the microfluidic ratchet device to measure the deformability of red blood cells stored in blood bags every 14 days over a span of 56 days. We observed that storage in blood bags generally prevented RBC deformability loss over the current standard 42-day storage window. However, between 42 and 56 days, the deformability loss profile varied dramatically between donors. In particular, we observed accelerated RBC deformability loss for a majority of male donors, but for none of the female donors. Together, our results suggest that RBC deformability loss could be used to screen for donors who can provide stable RBCs for sensitive transfusion recipients or to identify donors capable of providing RBCs that could be stored for longer than the current 42-day expiration window.

bioengineering↗

bioRxiv2021

Blood Unit Segments Accurately Represent the Biophysical Properties of Red Blood Cells in Blood Bags but Not Hemolysis

BACKGROUNDThe biophysical properties of red blood cells (RBCs) provide potential biomarkers for the quality of donated blood. Blood unit segments provide a simple and nondestructive way to sample RBCs in clinical studies of transfusion efficacy, but it is not known whether RBCs sampled from segments accurately represent the biophysical properties of RBCs in blood bags. STUDY DESIGN AND METHODSRBCs were sampled from blood bags and segments every two weeks during 8 weeks of storage at 4 {degrees}C. RBC deformability was measured by deformabilitybased sorting using the microfluidic ratchet device in order to derive a rigidity score. Standard hematological parameters, including mean corpuscular volume (MCV), red cell distribution width (RDW), mean cell hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and hemolysis were measured at the same time points. RESULTSDeformability of RBCs stored in blood bags was retained over 4 weeks storage but a progressive loss of deformability was observed at weeks 6 and 8. This trend was mirrored in blood unit segments with a strong correlation to the blood bag data. Strong correlations were also observed between blood bag and segment for MCV, MCHC and MCH, but not for hemolysis. CONCLUSIONRBCs sampled from blood unit segments accurately represents the biophysical properties of RBCs in blood bags, but not hemolysis. Blood unit segments provide a simple and non-destructive sample for measuring RBC biophysical properties in clinical studies.

bioengineering↗