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\begin{document}

\title{Chromosome-scale assembly and whole-genome sequencing of 266 giant panda
roundworms provide insights into their evolution, adaptation and
potential drug targets}



\author[1]{Lei Han}%
\author[2]{Tianming Lan}%
\author[3]{Desheng Li}%
\author[4]{Haimeng Li}%
\author[5]{Linhua Deng}%
\author[3]{Zhiwei Peng}%
\author[6]{Shaowen  He}%
\author[1]{Yanqiang Zhou}%
\author[1]{Ruobing Han}%
\author[1]{Lingling Li}%
\author[1]{Yaxian Lu}%
\author[2]{Haorong Lu}%
\author[7]{Qing Wang}%
\author[8]{Shangchen Yang}%
\author[2]{Yixin Zhu}%
\author[2]{Yunting Huang}%
\author[9]{Xiaofang Cheng}%
\author[2]{Jieyao Yu}%
\author[1]{Yulong Wang}%
\author[1]{Heting Sun}%
\author[1]{Hongliang Chai}%
\author[2]{Huanming Yang}%
\author[10]{Xun  Xu}%
\author[11]{Michael Lisby}%
\author[1]{Quan Liu}%
\author[3]{Karsten  Kristiansen}%
\author[12]{Huan Liu}%
\author[1]{Zhijun Hou}%
\affil[1]{Northeast Forestry University}%
\affil[2]{State Key Laboratory of Agricultural Genomics, BGI-Shenzhen}%
\affil[3]{Affiliation not available}%
\affil[4]{BGI-Shenzhen}%
\affil[5]{Key Laboratory of SFGA on Conservation Biology of Rare Animals in the Giant Panda National Park (CCRCGP)}%
\affil[6]{Foping National Nature Reserve}%
\affil[7]{University of Chinese Academy of Sciences}%
\affil[8]{Zhejiang University}%
\affil[9]{MGI, BGI-Shenzhen}%
\affil[10]{BGI}%
\affil[11]{Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen}%
\affil[12]{University of Copenhagen}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Helminth diseases have long been a threat to the health of humans and
animals. Roundworms are important organisms for studying parasitic
mechanisms, disease transmission and prevention. The study of parasites
in the living fossil giant panda is of great significance for
understanding the adaptation mechanism of roundworms to the host. Here,
we report a high-quality chromosome-scale genome of Baylisascaris
schroederi with a genome size of 262 Mb and 19,291 predicted
protein-coding genes. We found a significant expansion of genes related
to epidermal chitin synthesis and environmental information processing
in roundworms genome. Furthermore, we demonstrated unique genes involved
in essential amino acid metabolism in the B. schroederi genome, inferred
to be essential for the adaptation to the giant panda-specific diet. In
addition, under different deworming pressures, we found that four
resistance-related genes (glc-1, nrf-6, bre-4 and ced-7) were under
strong positive selection in captive population. Finally, 23 known drug
targets and 47 potential target proteins were identified. The genome
provides a unique reference for inferring the early evolution of
roundworms and the mechanisms underlying adaptive. Population genetic
analysis and drug prediction provide insights for revealing the impact
of deworming history on population genetic structure and prevention.%
\end{abstract}%



\sloppy


Chromosome-scale assembly and whole-genome sequencing of 266 giant panda
roundworms provide insights into their evolution, adaptation and
potential drug targets

Lei Han\textsuperscript{1,5+}, Tianming Lan\textsuperscript{2,3+},
Desheng Li\textsuperscript{4+}, Haimeng Li\textsuperscript{2,7}, Linhua
Deng\textsuperscript{4}, Zhiwei Peng\textsuperscript{1}, Shaowen
He\textsuperscript{15}, Yanqiang Zhou\textsuperscript{1}, Ruobing
Han\textsuperscript{1}, Lingling Li\textsuperscript{1}, Yaxian
Lu\textsuperscript{1}, Haorong Lu\textsuperscript{8,9}, Qing
Wang\textsuperscript{12}, Shangchen Yang\textsuperscript{10}, Yixin
Zhu\textsuperscript{12}, Yunting Huang\textsuperscript{8,9}, Xiaofang
Cheng\textsuperscript{11}, Jieyao Yu\textsuperscript{8,9}, Yulong
Wang\textsuperscript{1}, Heting Sun\textsuperscript{13}, Hongliang
Chai\textsuperscript{1}, Huanming Yang\textsuperscript{2}, Xun
Xu\textsuperscript{2,8}, Michael Lisby\textsuperscript{3}, Quan
Liu\textsuperscript{1,6*}, Karsten Kristiansen\textsuperscript{3,14*},
Huan Liu\textsuperscript{2,3*}, Zhijun Hou\textsuperscript{1,5*}

\textsuperscript{1}College of Wildlife and Protected Area, Northeast
Forestry University, Harbin~150040, China

\textsuperscript{2}State Key Laboratory of Agricultural Genomics,
BGI-Shenzhen, Shenzhen 518083, China

\textsuperscript{3}Laboratory of Genomics and Molecular Biomedicine,
Department of Biology, University of Copenhagen, DK-2100 Copenhagen,
Denmark

\textsuperscript{4}Key Laboratory of SFGA on Conservation Biology of
Rare Animals in the Giant Panda National Park (CCRCGP), Sichuan 611800,
China

\textsuperscript{5}Key Laboratory of Wildlife Conservation, China State
Forestry Administration, Harbin 150040, China

\textsuperscript{6}School of Life Sciences and Engineering, Foshan
University, Foshan, Guangdong Province, China

\textsuperscript{7}School of Future Technology, University of Chinese
Academy of Sciences, Beijing 100049, China

\textsuperscript{8}Guangdong Provincial Key Laboratory of Genome Read
and Write, BGI-Shenzhen, Shenzhen 518120, China

\textsuperscript{9}China National GeneBank, BGI-Shenzhen, Shenzhen
518083, China.

\textsuperscript{10}College of Life Sciences, Zhejiang University,
Hangzhou 310058, China

\textsuperscript{11}MGI, BGI-Shenzhen, Shenzhen 518083, China

\textsuperscript{12}BGI Education Center,University of Chinese Academy
of Sciences,Shenzhen 518083,China

\textsuperscript{13}General Station for Surveillance of Wildlife
Diseases, National Forestry and Grassland Administration, Harbin~150040,
China

\textsuperscript{14}Qingdao-Europe Advanced Institute for Life Sciences,
Qingdao 266555, China

\textsuperscript{15}Foping National Nature Reserve, Hanzhong 723400,
China

\textsuperscript{+}These authors contributed equally to this work.

\textsuperscript{*}Corresponding authors:
houzhijundz@163.com,\emph{liuhuan@genomics.cn},\emph{kk@bio.ku.dk}
and\emph{liuquan1973@hotmail.com}

\textbf{Abstract}

Helminth diseases have long been a threat to the health of humans and
animals. Roundworms are important organisms for studying parasitic
mechanisms, disease transmission and prevention. The study of parasites
in the living fossil giant panda is of great significance for
understanding the adaptation mechanism of roundworms to the host. Here,
we report a high-quality chromosome-scale genome of \emph{Baylisascaris
schroederi} with a genome size of 262 Mb and 19,291 predicted
protein-coding genes. We found a significant expansion of genes related
to epidermal chitin synthesis and environmental information processing
in roundworms genome. Furthermore, we demonstrated unique genes involved
in essential amino acid metabolism in the \emph{B. schroederi} genome,
inferred to be essential for the adaptation to the giant panda-specific
diet. In addition, under different deworming pressures, we found that
four resistance-related genes (\emph{glc-1} , \emph{nrf-6} ,
\emph{bre-4}and \emph{ced-7} ) were under strong positive selection in
captive population. Finally, 23 known drug targets and 47 potential
target proteins were identified. The genome provides a unique reference
for inferring the early evolution of roundworms and the mechanisms
underlying adaptive. Population genetic analysis and drug prediction
provide insights for revealing the impact of deworming history on
population genetic structure and prevention.

\textbf{Keywords:} \emph{Baylisascaris schroederi} , Roundworms,
Adaptation, Genetic diversity, Anthelmintics

\textbf{1 \textbar{} INTRODUCTION}

Parasitic ascariasis has long been a threat to the health of humans,
livestock and wildlife worldwide(Hotez, Fenwick, Savioli, \& Molyneux,
2009). With the expansion of towns, cities, and the wild land-urban
interface, geographic isolation is no longer an effective barrier for
transmission of helminth infections. As a result, the risks for
transmission of diseases once isolated in wildlife have never been
greater(K. Kazacos \& W. M. Boyce, 1989). Due to its wide distribution
and long incubation period, soil-transmitted helminth eggs are easily
transmitted between wildlife and livestock, and even to humans through
contaminated feces or soil. In-depth studies of helminths in wildlife
can provide information of relevance for identifying and detecting
pathogens and instigate appropriate actions to deal with possible risks
with broad and far-reaching implications for wildlife and human health.

\emph{B. schroederi} , a parasitic nematode specific for the giant panda
(\emph{Ailuropoda melanoleuca} ), is a soil-transmitted nematode and can
directly infect the giant panda without passing through an intermediate
host(Bethony et al., 2006; De Silva et al., 2003). Baylisascaris species
also cause infection as patent or latent larval migrants (LM) in a
variety of mammals(K. Kazacos \& W. M. Boyce, 1989), birds(Wolf, Lock,
Carpenter, \& Garner, 2007) and humans(Murray, 2002; Wise, Sorvillo,
Shafir, Ash, \& Berlin, 2005), and are therefore considered zoonotic
parasites with potential public health and safety risks. Its eggs can
develop directly into infective larvae under appropriate conditions.
Larvae can migrate to multiple organs of the host and cause visceral
larval migrans (VLM), ocular larva migrans (OLM), neural larva migrans
(NLM) and even severe pneumonia and hepatitis(K. R. Kazacos \& W. M.
Boyce, 1989; Papini, Renzoni, Malloggi, \& Casarosa, 1995; Wildt, Zhang,
Zhang, Janssen, \& Ellis, 2006; L. Zhang et al., 2011). A large number
of roundworm infections can in addition cause severe baylisascariasis,
intestinal blockage, and even fatal bowel rupture(Schaul, 2006; GY Yang,
1998). Compared with other roundworms, \emph{B. schroederi} is smaller
in size and is mainly found in the small intestine of giant pandas.
Giant pandas have typical carnivorous intestinal characteristics, but
eat bamboo, a diet with low digestibility and absorption. This
challenges the nutrient absorption of\emph{B. schroederi} for survival
in the small intestine. Based on available epidemiological data of the
giant panda, \emph{B. schroederi}is the leading cause of death from
primary and secondary infection in wild and captive populations(H. Hu et
al., 2018; D. Li et al., 2014). Moreover, the problem of increased
resistance to anthelmintics is likely to be seriously underestimated.
Giant pandas in captivity are regularly dewormed (every 60 days).
According to investigations, \emph{B. schroederi} eggs can still be
detected in the feces 10 to 15 days after treatment with
anthelmintics(D. Li, He, \& Deng, 2015), indicating that a development
of drug-resistant subtypes had occurred in the \emph{B. schroederi}
population, and that \emph{B. schroederi} variants with resistance to a
variety of anthelmintics had survived. These variants may potentially
become anthelmintic-resistant pathogens.

Although \emph{B. schroederi} poses threats to both wild and captive
giant pandas, current studies are limited to morphological and single or
multiple gene analyses, preventing in-depth exploration of genetic
mechanisms of adaptations and further prevention and control of
infections(Xie et al.). Here, we report a chromosome-scale reference
genome of \emph{B. schroederi} , which is also the first
chromosome-scale reference genome of ascaridoids. Based on the genome,
we explored possible genetic mechanisms of the adaptation of \emph{B.
schroederi} to the intestinal environment, especially the specific
bamboo diet of the giant panda, as well as the potential genetic basis
of drug resistance. Finally, potential drug target proteins were
identified, which provides new insights into the potential disease
management of\emph{Baylisascaris} and related roundworms.

\textbf{2 \textbar{} MATERIALS AND METHODS}

\textbf{2.1 \textbar{} Samples}

All specimens of Sichuan \emph{B. schroederi} population were collected
from the giant panda (\emph{Ailuropoda melanoleuca} ) with naturally
acquired infections from the China Conservation and Research Center for
Giant Panda. Samples of Qinling \emph{B. schroederi} population were
obtained from the intestines of individuals who died after wild panda
rescues in the Foping National Nature Reserve (Qinling Mountains)
failed. Roundworms were washed extensively in sterile physiological
saline (37 \textsuperscript{o}C), sexes separated, snap-frozen and
transport with dry ice and then stored at -80 \textsuperscript{o}C until
use. Several specimens were stored in RNA preservation solution for
transcriptome sequencing. All experimental designs and nematodes
handling were approved by the Institutional Animal Care and Use
Committee of Northeast Forestry University.

\textbf{2.2 \textbar{} Nucleic acid isolation, library construction and
sequencing}

We firstly removed the intestines of adult individuals under an inverted
microscope to prevent intestinal microorganism contamination. Then,
total genomic DNA was isolated from them using a sodium-dodecyl
sulphate/proteinase K digestion(Gasser et al., 2006) followed by
phenol-chloroform extraction and ethanol precipitation. For PacBio
sequencing, 20-Kb SMRTbell libraries were constructed using the SMRTbell
Template Prep Kit 1.0 (Pacific Biosciences, USA) and the SMRTbell Damage
Repair Kit (Pacific Biosciences), according to the manufacturer' s
instructions. The Pacbio sequencing was performed with 1 SMRT cell on
the PacBio SMRTplatform. For genome resequencing, 100-bp pair-end
libraries were constructed and sequenced a whole genome sequence (WGS)
short reads on DNBSEQ-T1 platform. For RNA-seq, RNA was isolated
separately from an adult female and a male individual of\emph{B.
schroederi} using TRlzol reagent (Invitrogen, USA) according to the
manufacturer's instructions and RNA yields were estimated
spectrophotometrically (NanoDrop 1000), and integrity was verified using
a Bioanalyzer. A total of 22Gb raw fastq data of female and 27 Gb raw
fastq data of male were obtained. All transcripts were used to assess
the completeness of the genome assembly and assist genome annotation.

\textbf{2.3 \textbar{} Genome assembly and quality control}

To obtain high-quality genome, WGS data were used for genome survey
analysis to estimate essential genome information, including genome
size, level of heterozygosity, and repeat content. We used GCE
pipeline(Liu et al., 2013) to estimate the genome size of \emph{B.
schroederi} before genome assembly. We generated a total of 29.32 Gb
(about 110\selectlanguage{ngerman}×) raw PacBio long reads. First, we used Blastall
(v2.2.26)(Camacho et al., 2009) to compare the raw data with the NCBI
database to confirm that the DNA of the sequenced samples was not
contaminated by other species. Then we counted the length frequency
distribution of TGS reads to evaluate the sequencing quality and provide
a reference for the setting of subsequent assembly parameters.

The \emph{B. schroederi} genome was assembled using a
``correct-then-assemble'' strategy. First, NextDenovo
(v2.0-beta.1;\emph{https://github.com/Nextomics/NextDenovo}) was used to
correct and assemble a draft genome. Arrow algorithm was then used to
carry out a second round of correction for this assembly. NextPolish
(v1.0.5)(J. Hu, Fan, Sun, \& Liu, 2020) was further used for genome
polishing by using the WGS data. We then obtained a primary genome
assembly with 639 contigs with N50 of 1.27 Mb. To finally ligate the
scaffolds to chromosomes, Hi-C technology(Lieberman-Aiden et al., 2009)
was used to capture the chromosome conformations. 105Gb
(\textasciitilde{}400 X) Hi-C sequencing data were generated from a
single Hi-C library which was constructed as previously described. All
Hi-C reads were first mapped against our assembled genome using BWA
(v0.7.13-r1126)(H. Li \& Durbin, 2010) with parameters ``bwa mem -t 16
-k 19 -a -V ''. HiC-Pro pipeline(Servant et al., 2015) was then used for
filtering the mapping result, leaving 645 million valid read pairs.
Next, Juicer v1.5.7 software was used for auxiliary assembly: 1) both
duplicates and near-duplicates are removed; 2) read pairs that aligned
to three or more locations are set aside(Durand, Shamim, et al., 2016).
Then, 3D-DNA, a custom computational pipeline, was applied for
correcting misassembles, anchor, order and orient fragments of
DNA(Dudchenko et al., 2017). Finally, files generated from the 3D-DNA
were loaded into the visual software Juicebox Assembly Tools module
v1.11.08 for correction and review(Dudchenko et al., 2018; Durand,
Robinson, et al., 2016). Contigs from the \emph{B. schroederi} were
successfully clustered into 21 groups, which were further ordered and
oriented into Pseudochromosomes (Figure S2). The completeness of the
genome were evaluated using sets of BUSCO with genome mode and lineage
data from nematode and eukaryote, respectively(Simão, Waterhouse,
Panagiotis, Kriventseva, \& Zdobnov, 2015).

\textbf{2.4 \textbar{} Detection and classification of repetitive
elements}

For \emph{de novo} identification of repeat elements, we constructed a
transposable element (TE) library of the \emph{B. schroederi} genome to
identified types of repeat elements by using Tandem Repeat Finder
(TRF)(Benson \& G.), LTR\_FINDER(X. Zhao \& Hao, 2007) and RepeatModeler
(v1.0.8)(Smit, Hubley, \& Green, 2015). RepeatMasker(Tarailo-Graovac \&
Chen, 2009) and RepeatProteinMask(Tempel, 2012) were used to search the
genome sequences for known repeat elements, with the genome sequences
used as queries against the Repbase database(Jurka et al.).

\textbf{2.5 \textbar{} Protein-coding genes prediction and function
annotation}

A combined strategy of de~novo gene prediction, homology-based search
and RNA sequencing-aided annotation were used to perform gene
prediction. For homology-based annotation, we selected the
protein-coding sequences of five homologous species (\emph{Brugia
malayi} , \emph{C. elegans} , \emph{Pristionchus pacificus}
,\emph{Steinernema carpocapsae} and \emph{T. canis} ) from NCBI
(https://www.ncbi.nlm.nih.gov/). For RNA-based prediction, a male and a
female transcriptome sequence was aligned to the genome for assembly
using TopHat (v2.1.0)(Trapnell, Pachter, \& Salzberg) plus Trinity
(v2.0.6)(Haas et al.) strategy. PASApipeline (v.2.1.0) was applied to
predict gene structure after which the inferred gene structures were
used in AUGUSTUS (v.3.2.3)(Mario et al., 2006) to train gene models
based on transcript evidence. In addition, genome sequence was analyzed
by the program GeneMark (v1.0)(John \& Mark, 2005) utilizing
unsupervised training to build a hidden Markov model. The consistent
gene sets were generated by combining all above evidence using MAKER
(v.2.31.8)(Campbell, Law, Holt, Stein, \& Yandell, 2013). All gene
evidence was merged to form a comprehensive and non-redundant gene set
using EvidenceModeler (v1.1.1, EVM)(Haas et al., 2008).

In order to perform gene functional annotation, we aligned above gene
sets against several known databases, including SwissProt, TrEMBL, KEGG,
COG andNR. GO information was obtained through Blast2go (v.2.5.0)(Conesa
et al., 2005). Furthermore, the mitochondrial genome was assembled by
blasting with \emph{B. schroederi} 's mtDNA sequence from NCBI
database(NC\_015927.1)(Xie et al., 2011). The mitochondrial genome was
annotated on GeSeq online
(\emph{https://chlorobox.mpimp-golm.mpg.de/geseq.html}) using homologous
gene alignment(Michael et al., 2017). Four types of Non-coding RNA
(ncRNA; including tRNA, snRNA, miRNA, and rRNA) were predicted.
tRNAscan-SE (v1.3.1)(Lowe \& Eddy, 1997) were used to predict tRNAs. We
aligned \emph{B. schroederi} genome against Rfam (v12.0)(Kalvari et al.,
2018) database and invertebrate rRNA database to predict snRNA, miRNA
and rRNA, respectively.

\textbf{2.6 \textbar{} Proteases, protease inhibitors (PI) and
Excretory/secretory proteins (EPs)}

Proteases and PIs were annotated using MEROPS batch-BLAST(Rawlings \&
Morton, 2008) against MEROPS (E\textless{}1e\textsuperscript{-10}). The
conventional secretion proteins and non-conventional secretion proteins
were identified. SignalP-5.0 Server
(http://www.cbs.dtu.dk/services/SignalP/) and TMHMM Server v2.0
(http://www.cbs.dtu.dk/services/TMHMM/) were used to predict the signal
peptides and transmembrane regions of proteins, respectively. The
proteins with NNscore \textgreater{} 0.9 were selected as
non-conventional protein secretion in the results of SecretomeP 2.0
Server (\emph{http://www.cbs.dtu.dk/services/SecretomeP/})
identification.

\textbf{2.7 \textbar{} Reconstruction of phylogeny and evolutional
analysis among genomes from six nematodes}

Genomes and annotation files of three roundworms parasitic on mammals
(\emph{A. suum} , \emph{T. canis} and \emph{P. univalens} ), one
free-living nematode (\emph{C. elegans} ) and one nematode parasitic on
the root of a plant (\emph{M. hapla} ) were download from NCBI or
WormBase database (Table S10)(Michelle, Dubaj, Price, Daryl, \& Hurd,
2019). The syntenic analysis both at whole-genome nucleotide-level and
protein-level were performed by aligning 5 nematodes genomes to our
assembled B. \emph{schroederi} genome by using MCScanX(Y. Wang et al.,
2012) software. Orthogroups among six nematodes are define using TreeFam
(v4.0)(H. Li et al., 2006). Next, the protein sequences from each family
were aligned using MUSCLE (v3.8.31)(Edgar, 2004) with default
parameters. The conserved CDS alignments were extracted by
Gblocks(Gerard \& Jose, 2007). We select single-copy gene families to
construct phylogenetic trees based on maximum likelihood using RAxML
(v8.2.4)(Alexandros, 2014) with PROTCATGTR nucleotide substitution model
with 500 bootstrap replicates.

Using the divergent time between \emph{C. elegans} and \emph{A.
suum,}which was calculated based on fossil evidence, as the reference
time points(Mcgill, Fitzpatrick, Pisani, \& Burnell, 2017), we estimated
the divergent time between each species by MCMCtree from the PAML
(v4.8)(Bo \& Yang, 2013) package with default parameters.

\textbf{2.8 \textbar{} Expanded and contracted gene families}

Based on the phylogenetic tree we constructed using the 2451 single-copy
genes, we explored significant expanded and contracted gene families in
six nematodes (\emph{A. suum} , \emph{B. schroederi} , \emph{T. canis}
,\emph{P. univalens} , \emph{C. elegans} and \emph{M. hapla} ). Gene
family expansion and contraction analyses were performed using CAFÉ
(v4.2)(Bie, Cristianini, Demuth, \& Hahn, 2006). We used a median gene
number to estimate the changes in gene family size. The overall P-value
of each gene family were calculated and the details of each branch and
node with the exact P-values of each significant overall P-value (\selectlanguage{english}[?]
0.01) gene family were also calculated. Here we performed analysis in
two levels: 1) Comparing gene families of roundworms to other nematode'
to determine the expanded and contracted genes in roundworms; 2)
Comparing the\emph{B. schroederi} and other three roundworms (\emph{A.
suum} ,\emph{P. univalens} and \emph{T. canis} ) to identify gain and
loss of genes in \emph{B. schroederi} , and then further explain their
differentiation within the roundworm branch.

\textbf{2.9 \textbar{} Identification of positively selected genes
(PSGs)}

Based on gene families retrieved from the TreeFam, we identify PSGs
between \emph{B. schroederi} and other five species (\emph{A. suum}
,\emph{T. canis} , \emph{P. univalens} , \emph{C. elegans} and \emph{M.
hapla} ). The branch-site model of CodeML in PAML (v4.8)(Bo \& Yang,
2013) was used to detect potential PSGs. Using the null hypothesis and
the alternative hypothesis to estimate whether the dN/dS (\selectlanguage{greek}ω\selectlanguage{english}) value of
the foreground branch (Marked as \emph{B. schroederi} ) was larger than
1 or not. Then performing likelihood ratio test (LRT) using R
``chisq.test()'' function to calculate chi-squared distributions with 1
degree of freedom. The PSG was identified by meeting the requirements of
a corrected P-value (\textless{} 0.05) and contained at least one
positively selected site with a posterior probability [?]1.

\textbf{2.10 \textbar{} Identification of detoxification-related gene
families}

Seven detoxification-related gene families of four roundworms were
identified by the HMMER3 (http://hmmer.janelia.org/) software, including
ABC domain (PF00005), Cytochrome P450 (\emph{CYP} ) domain (PF00067),
GST domain (PF00045), CHIA domain (PF00704), PTCHD domain (PF02460) and
PTP domain (PF00782). The domain file is used as first template for
scanning the gene family, the output genes were filtered out with
E-value lower than 1e\textsuperscript{-10}, then the filtered genes were
used as second template for scanning the gene family. Again, the second
output genes were filtered out with E-value lower than
1e\textsuperscript{-10}.

\textbf{2.11 \textbar{} The change of effective population size on the
function of time}

We inferred the demographic history of the \emph{B. schroederi} by use
of WGS data generated by DNBSEQ-T1 from one individual. We
simultaneously perform the same analysis on a giant panda by using
resequencing short reads of an individual download from SRA database
(accession SRA053353). For this analysis, we used BWA (v0.7.13-r1126)(H.
Li \& Durbin, 2009) to map the clean reads to each genome with the
default parameters. Next, the PSMC method(H. Li \& Durbin, 2011) was
used to evaluate dynamic change of effective population size (\emph{Ne}
) of \emph{B. schroederi} and giant panda. Following Li's procedure(H.
Li \& Durbin, 2011), we applied a bootstrapping approach, repeat
sampling 100 times to estimate the variance of simulated results for
both\emph{B. schroederi} and giant panda. We used 0.17 and 12 years per
generation (g) and mutation rate (\selectlanguage{greek}\emph{μ} \selectlanguage{english}) of
9\selectlanguage{ngerman}×10\textsuperscript{-9}and 1.29×10\textsuperscript{-8} for \emph{c} and
giant panda, respectively(Cutter, 2008). Since fluctuations in the
effective population size of giant pandas have been reported to closely
reflect changes in climate and atmospheric dust(S. Zhao et al., 2013),
we added the mass accumulation rate (MAR) of Chinese loess over the past
250,000 years for comparison. In addition, we implemented the
MSMC2(Schiffels \& Durbin, 2014) which can infer the recent effective
population size history. We phased all SNPs of each individual by using
beagle (v5.0)(Browning \& Browning, 2007)\selectlanguage{english},then it calculated used the
following parameters: -i 20 -t 6 -p '10*1+15*2'. The mutation rate
(\selectlanguage{greek}\emph{μ} \selectlanguage{english}) of \emph{B. schroederi} for MSMC2 were used the same values
as for PSMC.

\textbf{2.12 \textbar{} Population structure of \emph{B. schroederi} in
Qinling and Sichuan}

A total of 240 samples collected from individuals in captivity and 26
samples from individuals in wild were re-sequenced using the
DNBSEQ-T1\&T5 platform. High-quality reads were aligned to the reference
genome using BWA-MEM (0.7.13-r1126)(H. Li \& Durbin, 2010) with default
parameters. SAMtools (v0.1.19)(H. Li et al., 2009) and Genome Analysis
Toolkit (GATK v 4.0.3.0)(Depristo, Banks, Poplin, Garimella, \& Daly,
2011) were used to obtain the SNP set within the population. Hard
filtering was applied to the raw variant set using ''QD \textless{} 2.0
\textbar{}\textbar{} FS \textgreater{} 60.0 \textbar{}\textbar{} MQ
\textless{} 40.0 \textbar{}\textbar{} MQRankSum \textless{} -12.5
\textbar{}\textbar{} ReadPosRankSum \textless{} -8.0'' --filter-name
''snp\_filter''. SNPs with \textgreater{}0.5\% missing data or
\textless{}0.01 minor allele frequency (MAF) were filtered out using
vcftools (v0.1.12a)(Danecek et al., 2011). PCA analysis of SNPs was
carried out using EIGENSOFT(Nick et al., 2006) software, and the
population clustering analysis was conducted in PLINK(Purcell et al.,
2007). We used the whole-genome SNPs to construct the ML phylogenetic
tree with 1000 bootstrap using iqtree (v1.6.12)(Lam-Tung, Schmidt,
Arndt, \& Quang, 2015), and using an genome sequence information
of\emph{P. univalens} as an outgroup. Population structure of all was
analyzed using the ADMIXTURE (v1.3.0) program with a block-relaxation
algorithm. To explore the convergence of individuals, we predefined the
number of genetic clusters K from 2 to 5 and ran the cross-validation
error (CV) procedure.

\textbf{2.13 \textbar{} Recent nature selection analysis}

Extended Haplotype Homozygosity (EHH) and iHS methods was used for
detecting SNPs under strong positive selection of Captive and wild
population, respectively(Mathieu \& Renaud, 2012). We searched for genes
in the 5-kb flanking region from both sides of candidate SNPs, and
calculate the accumulated iHS scores by adding each iHS score of the top
0.5\% SNPs. Next, to uncover genetic variants involved in adaptation of
different environments, we performed comparisons between Captive and
wild population. The XP-EHH method was used to detect selective sweeps
using the R package rehh
(v3.1.2;\emph{https://cran.r-project.org/web/packages/rehh/vignettes/rehh.html}).
We then split the genome into non-overlapping segments of 50 kb to use
the maximum (positive) XP-EHH score of all SNPs. The regions with P
values less than 0.01 were considered significant signals in the
population of interest.

\textbf{2.14 \textbar{} Known and potential drug targets}

All compound-related proteins were searched against target proteins from
ChEMBL v26(Anna et al.) using BLASTP (E[?]1\selectlanguage{ngerman}×10\selectlanguage{english}\textsuperscript{-10}). We
screened out all types of single proteins in the ChEMBL database for
blasting. Known drug target were identified from available publications
and by searching for `anthelmintics' in the DrugBank(S et al., 2017)
database. For potential drug targets, we screen out all the single
protein targets as described(Coghlan, Mutowo, O'Boyle, Lomax, \&
Berriman, 2018) and made adjustments to evaluate each gene. We set a
score of `0/1' considering six main factors: 1. Similarity with ChEMBL
drug targets and a highly conserved alignment (\textgreater{}80\%); 2.
Lack of human homologues; 3. Related to lethal, L3 arrest, flaccid, molt
defect and sterile phenotypes. Lethal phenotypes were identified in
WormBase WS240; 4. Whether the protein was a predicted chokepoint
enzyme(Tyagi, Seshadri, Parkinson, \& Mitreva, 2018); 5. The protein was
predicted as an excretory/secretory protein (EP); (6) The protein had a
structure in the PDBe(Sameer et al., 2016). In order to make the
screening most efficient, we searched for commercially available
compounds against the target protein in ZINC15(Sterling \& Irwin, 2015).
Finally, we selected compounds approved in phase III or above as
suggested chemical compounds.

\textbf{3 \textbar{} RESULTS}

\textbf{3.1 \textbar{} Genome assembly, annotation and evaluation}

A total of 29 Gb (110 X) PacBio long reads were generated (Table S1).
The genome size was estimated to be 266.8 Mb based on K-mer depth
distribution analysis (Tables S2 and Fig. S1), and the size of the
assembled \emph{B. schroederi} genome reached 262 Mb, accounting for
98.48\% of the estimated genome. This genome contained 106 scaffolds, 21
of which were superscaffolds ligated using 105 Gb (\textasciitilde{}386
X) Hi-C sequencing data (Fig. 1a; Fig. S2). The total length of these 21
superscaffolds reached \textasciitilde{}263 Mb, accounting for 98.67\%
of the whole genome (Table S3). The final scaffold N50 was 12.69 Mb
(Table 1), which is significantly better than the published genome(Y. Hu
et al., 2020). The GC-depth distribution (Fig. S3a and S3b) further
showed that most genomic regions have a GC content narrowly centered
around 37\%, which is similar to that of other three
roundworms(''Ascaris suum draft genome,'' 2011; Zhu et al., 2015)
(\emph{Ascaris suum} ,\emph{Parascaris univalens} and \emph{Toxocara
canis} ; Table 1). Benchmarking Universal Single-Copy Orthologs (BUSCO)
scores against nematode and eukaryote databases were 91.9\% and 93.4\%,
respectively (Fig. S4), reflecting the highest genome completeness among
published roundworm genomes (Table 1). 19,291 protein-coding genes were
predicted via \emph{ab initio,} homology-based and RNA sequencing-aided
methods (see Methods)(''serine-threonine kinase KIN-29 modulates TGFbeta
signaling and regulates body size formation,''). Kyoto encyclopedia of
genes and genomes (KEGG), clusters of orthologous groups of proteins
(COG), TrEMBL, gene ontology (GO), Swissprot and InterPro (Fig. S5 and
Fig. S6). The average length of coding sequences (CDS) was 1,052 bp with
an average of 6.87 exons per gene, which is similar to that of other
related roundworms (Table S4). Furthermore, a 14,767 bp mitochondrial
genome was identified, containing 12 genes encoding proteins, 2 genes
encoding rRNAs, and 22 genes encoding tRNAs (see methods; Table S5 and
Fig. S7). To evaluate the completeness of the predicted protein-coding
genes, we compared the length distributions of mRNA, CDS, exons and
introns in\emph{B. schroederi} with those in other five nematodes (Fig.
S8 and Fig. S9).

Total repeats (DNA transposons and RNA transposons) accounted for
11.76\% of the genome (Table S6-S8 and Fig. S10). Huge variation in the
proportion of repeat content is found among published nematode genomes
(from 1\% to 48\%) (Berriman et al., 2009; International, Helminth,
Genomes, \& Consortium, 2018; Schiffer, Kroiher, Kraus, Koutsovoulos, \&
Schierenberg, 2013). Transposable elements (TEs) account for 9.51\% of
the \emph{B. schroederi} genome (Table S8), while TEs constitute 4.4\%
and 13.5\% in the genomes of \emph{A. suum(''Ascaris suum draft
genome,'' 2011)} and \emph{T. canis(Zhu et al., 2015)} , respectively.
We identified a significant expansion of DNA transposons in \emph{B.
schroederi} compared to \emph{T. canis} (Zhu et al., 2015) and \emph{A.
suum} (''Ascaris suum draft genome,'' 2011) (Supplementary Data 1).
There are at least 64 DNA transposon families of which CMC-EnSpm, DNA
and MULE-MuDR dominated the genome. We identified 17 long terminal
repeats (LTRs) retrotransposon and 41 non-LTRs retrotransposon families
(25 LINE and 16 SINE). Pao and Gypsy are the predominant LTRs, and CR1,
RTE-RTE and L2 are the predominant non-LTRs. The number and size of the
retrotransposon families were similar to those of other related
roundworms(''Ascaris suum draft genome,'' 2011; Ghedin et al., 2007; Zhu
et al., 2015).

\textbf{3.2 \textbar{} Development-related genes for key enzymes, ion
channels, receptors and secretome}

To understand the genetic basis for the adaptation of\emph{B.
schroederi} to the parasitic life, we assessed the abundance of several
major protein classes in \emph{B. schroederi} , \emph{A. suum} ,
\emph{P. univalens} , and \emph{T. canis} (Table S10 and Fig. S11b). A
genome-wide search enabled us to identify 62 G-protein coupled receptors
(GPCRs), 437 proteases and protease inhibitors (Supplementary Data 2).
Some chemoreceptor families, especially those differentially expressed
during the life cycle, were almost completely conserved among nematodes.
For example, the homologues of \emph{Caenorhabditis elegans}
\emph{daf-37}(GeneID: Baysch11898) and \emph{daf-38} (GeneID:
Baysch06944), which are known to mediate ascaroside signaling, are
expressed during the transition from dauer larvae to infective
larvae(Park et al., 2012). By comparing with the ligand-gated ion
channel gene set collected from wormBase, 65 genes were identified,
including members of the previously described nematode acetylcholine
receptor classes (\emph{deg-3} ,\emph{acr-16} , \emph{unc-29} and
\emph{unc-38} )(International et al., 2018). We predicted
excretory/secretory proteins (EPs) of \emph{B. schroederi} , and at
least 1,395 EPs (5.26\% of total protein) with diverse functions were
identified, including 1,046 conventionally secreted proteins and 349
nonconventionally secreted proteins (Supplementary Data 2).

\textbf{3.3 \textbar{} Evolutionary and phylogenetic relationships among
six nematodes}

We clustered the \emph{B. schroederi} gene models with the genes from
five other nematode genomes (\emph{A. suum} , \emph{P. univalens}
,\emph{T. canis} , \emph{C. elegans,} and \emph{Meloidogyne hapla} ;
Table S11). We found that the six nematodes share 3,906 homologous gene
families (Fig. 1c). In addition, four roundworms show high consistency
in the number of single-copy and multi-copy genes (Fig. S11c).
Collinearity results showed that although several roundworms are closely
related, collinearities among the genomes are low (Fig. 1b). The
proportions of collinearity between \emph{B. schroederi} and the other
three roundworms in the genome species are 35.37\% (vs \emph{T. canis}
), 43.86\% (vs \emph{A. suum} ), and 55.12\% (vs \emph{P. univalens} ),
respectively, which indicate that genetic differentiation among
roundworms is considerable.

We used 2,451 single-copy genes shared within the six nematode genomes
to reconstruct a phylogenetic tree (Fig. 1d). The relationships among
the six nematodes in the phylogenetic tree are consistent with a
previous study(International et al., 2018). \emph{B. schroederi} is
closely related to \emph{A. suum} and \emph{P. univalens} . According to
the TimeTree(Hedges, 2011) database and fossil evidence from \emph{A.
suum} and \emph{C. elegans} (Mcgill et al., 2017), we estimated the
divergence time to approximately 400-269 million years ago (Mya), and
the divergence time between the four roundworms is approximately 160-26
Mya (Fig. 1d). Among the four roundworms, \emph{T. canis} was identified
as the earliest branch to the other three roundworms (approximately 134
Mya).

\textbf{3.4 \textbar{} Expanded and contracted genes inAscariasis}

Compared with \emph{C. elegans} and \emph{M. hapla} , a large number of
genes have been lost or contracted in the branch of roundworms (Fig.
2a). Specifically, we found that 563 gene families are significantly
contracted (\emph{P} \textless{}0.05), with an average loss of 1.61
genes in each family (Table S12). However, roundworms also show
significant expansion in 29 gene families (\emph{P} \textless{}0.05). We
focused on the changes in gene number related to free life and those
related to parasitic life in nematodes. For both the expanded and
contracted gene families, KEGG enrichment analysis showed that pathways
related to tissue development, metabolism and environmental information
processing had undergone significant changes (Fig. S12a and S12b).
Interestingly, a similar expansion also appeared in the roundworm branch
compared to\emph{M. hapla} (Fig. S12c-d). For tissue development, we
found that the\emph{CPG-2} gene family is significantly expanded in the
roundworm branch (\emph{P} \textless{}0.01). In addition, a significant
expansion of the tight junctions (ko04530), phagosome pathway (ko04145)
and Rap1 signaling pathway (ko04015) was observed (\emph{P}
\textless{}0.01; Fig 2a). In relation to self-defense, consistent with a
previous study, we observed an expansion of the chymotrypsin/elastase
inhibitor gene family, which may be related to a protection of
roundworms from host proteases(International et al., 2018). In addition,
according to the copy number statistics of expanded gene families, the
actin family was significantly expanded among all four roundworms (Fig.
2b). GO enrichment analysis showed that the genes related to nematode
behavior and biological adhesion accounted for the most significant
difference among all gene families exhibiting expansions in roundworms
(Fig. 2c).

\textbf{3.5 \textbar{} Expanded or contracted gene families related to
the adaptation of \emph{B. schroederi}}

To better understand the adaptation to the specific habitat and
intestinal environment of the giant panda, we analyzed the expanded and
contracted genes in \emph{B. schroederi} compared with three roundworms
(\emph{A. suum} , \emph{P. univalens} and \emph{T. canis)} .

We identified expanded gene families with functions involved in striated
muscle contraction (GO:0006941), nematode larval development
(GO:0002119), larval feeding behavior (GO:0030536), chitin metabolic
process (GO:0006030) and actin cytoskeleton organization (GO:0030036).
The most significantly enriched GO term was the straight muscle
contraction, which was largely due to the highly significant expansion
of the actin family (Fig. 3a, 3d). KEGG enrichment showed that the
number of genes involved in metabolic pathways, including drug
metabolism (metabolism of xenobiotics by cytochrome P450,
ko00980;\emph{P} \textless{}0.01) and autoimmunity (ko05130,
ko05100;\emph{P} \textless{}0.01), exhibited significant changes (Fig.
3a). We observed an expansion of the gene family involved in positive
regulation of eating behavior (Fig. 3b). Finally, we found that \emph{B.
schroederi} has 654 unique annotated proteins, which were mainly
enriched in the synthesis and recycling pathways of essential amino
acids especially the degradation of valine, leucine and isoleucine,
(ko00280, \emph{P} \textless{}0.01; Fig. 3c).

\textbf{3.6 \textbar{} The significant expansion of actin family in
Ascariasis and positive selection genes (PSGs) in \emph{B. schroederi}}

The migration of roundworms is the main cause of VLM (Fig. 4a). Actin
polymerization is controlled by intracellular signals that are mediated
by small GTPases of the Rho family (\emph{RhoA} , \emph{Rac1} ,
and\emph{Cdc42} ; Fig. 4c). We observed a very significant expansion of
the actin family in the \emph{B. schroederi} genome (Fig. 3d).
Surprisingly, the three upstream regulators of actin (\emph{Rac1} ,
\emph{ROCK} and\emph{MLCK} ), were under strong positive selection
(\emph{P} \textless{}0.01). \emph{ROCK} phosphorylates the LIM-kinase
which then phosphorylates cofilin to promote rho-induced actin
cytoskeleton reorganization(Maekawa \& M., 1999). These finding suggest
a possible effect on myosin-actin interaction. Using the branch-site
model implemented in PAML, 475 genes in the \emph{B. schroederi} genome
were found to be under strong positive selection compared with other
three roundworms (Supplementary Data 3a). Compared with three
roundworms, the acetylcholine receptor subunit alpha-type deg-3
(P=6.5x10\textsuperscript{-3}), which is an important drug target(Jones,
Davis, Hodgkin, \& Sattelle, 2007), was shown to be under strong
positive selection in the \emph{B. schroederi} genome (Supplementary
Data 3a).

\textbf{3.7 \textbar{} Demographic history of \emph{B. schroederi}}

To reconstruct the demographic history of \emph{B. schroederi} , and
explore the relationship with the giant panda, we used the pairwise
sequentially Markovian coalescent (PSMC) model to estimate the changes
of the effective population size of both \emph{B. schroederi} and the
giant panda within the last one million year(H. Li \& Durbin, 2011).
PSMC analysis showed that the effective population sizes (\emph{Ne} )
of\emph{B. schroederi} and the host giant panda have almost exactly the
same trend from 300 Kya (thousand years ago) to 10 Kya (Fig. 5a), but
the change of effective population size of the roundworms showed a
slight lag. The effective population size of the giant panda declined
significantly during the last two Pleistocene glacial periods (300-130
Kya and 50-10 Kya), and the effective population size of the roundworm
also reached a historical low level. The most obvious change occurred in
the Greatest Lake Period (50-30 Kya), where the effective population
size of the two species reached their pinnacle(Jinchu \& Wei, 2004). In
addition, we applied Multiple Sequentially Markovian Coalescent (MSMC)
method(Schiffels \& Durbin, 2014) to infer the recent demographic events
of \emph{B. schroederi} and performed 5 repetitions (each repetition
uses 4 individuals per population). The effective population size of
roundworms showed a sharp decline in the last 10,000 years, which also
may be related to the host population dynamics. Studies have indicated
that human activities may have caused the decline of the giant panda
population in the past few thousand years, and the roundworm population
may be affected by this.

\textbf{3.8 \textbar{} Population structure of \emph{B.
schroederi}populations}

We carried out whole-genome resequencing of 266 samples, including 240
from captive pandas of the Sichuan subspecies and 26 samples from
individuals of the wild Qinling panda subspecies (Fig. 6a). The average
sequencing coverage and sequencing depth reached 97.91\% and 41-fold,
respectively (Table S1 and S13). A total of 6.32 million SNPs were
obtained after filtering (see methods). Principal components analysis
(PCA) supported the clear separation between \emph{B. schroederi} from
the captive Sichuan and the wild Qinling panda subspecies (Fig. 6b),
with PC1 separating the Qinling and Sichuan populations and PC2
separating the Sichuan population into two clusters (\emph{P}
\textless{}0.05). We constructed a phylogenetic tree using the maximum
likelihood (ML) method, which showed two distinct clusters in the whole
population, with all Qinling individuals forming a single cluster and
all Sichuan individuals forming another single cluster (Fig. 6c). In
addition, the results of structure analyses also indicated that there
were almost no shared ancestral components between the Qinling and
Sichuan populations, supporting the results from the phylogenetic tree
and the PCA (Fig. 6d). Although the two populations showed extremely
similar genetic diversity (Fig. S13 and Fig. S14), our results
consistently supported two distinct groups corresponding to the Sichuan
and Qinling populations.

\textbf{3.9 \textbar{} Recent positive selections in the}\emph{B.
schroederi}\textbf{populations}

We used integrated haplotype score (iHS) to detect genes under recent
natural selection in the captive and wild populations. A total of 29,553
SNPs in captive and 18,953 SNPs in wild were identified within the top
1\% iHS scores. By extending the 25 kb distance around the top 1\% SNPs,
filtering out SNPs in the non-gene regions, a total of 518 and 370 genes
were located in the positively selected regions in captive and wild
populations, respectively (Supplementary Data 3b and 3c). We further
calculated the distribution of nucleotide diversity on the 21
superscaffolds (Fig S13), and found that the genetic diversity in
regions around the glutamate-gated chloride channel alpha (\emph{glc-1}
, a receptor for anthelmintic ivermectin(Cook et al., 2006)), nose
resistant to fluoxetine protein 6 (\emph{nrf-6} , a fluoxetine (Prozac)
resistance gene(Choy, Kemner, \& Thomas, 2006; Fares \& Grant, 2002)),
ABC transporter ced-7 (\emph{ced-7} , phagocyte corpse)(Wu \& Horvitz,
1998) and \selectlanguage{greek}β-\selectlanguage{english}1,4-N-acetylgalactosaminyltransferase bre-4 (\emph{bre-4} ,
resistance to pore-forming toxin(Griffitts et al., 2003)) genes were
significantly lower than in the flanking genome regions (Fig. 7b).
Interestingly, the four genes were only found under positive selection
in the captive population, but not in the wild population. In addition,
we also used the cross-population extended haplotype homozygosity
(XP-EHH) method(Pardis C Sabeti et al., 2007) to screen for genes that
may have been positively selected by different deworming selection
pressures by comparing the captive and wild populations (Supplementary
Data 3d). Similarly, the genes encoding the multidrug resistance
protein\emph{pgp-3} (Xu et al., 1998) which is related to ivermectin
resistance,\emph{glc-1} , \emph{nrf-6} , cytochrome p450 (\emph{CYP} )
family members and other drug resistance related genes, were also
observed to be under strong positive selection in the captive population
(Fig. 7c).

\textbf{3.10 \textbar{} Identifying anthelmintic resistance-related gene
family and drug targets}

We use the HMMER3 software to scan several detoxification-related gene
families at the whole genome scale, including ATP dependent (\emph{ABC}
), cytochrome P450 (CYP), glutathione s-transferase (GST), glycoside
hydrolase family 18 (CHIA), patched family (PTCHD) and protein tyrosine
phosphatase family (PTP) (Fig S11d). A total of 97
\emph{ABC}transporters, multipass membrane proteins, were identified in
\emph{B. schroederi} , and the average number of \emph{ABC} transporter
genes in roundworms was greater than that in \emph{C. elegans}
(60)(Schumacher \& Benndorf, 2017). The high-quality genome data of
\emph{B. schroederi}provides an opportunity to identify biologically
active anthelmintic compounds. On the one hand, it enables
identification of the targets of existing anthelmintics, on the other
hand, it also enables identification of new potential targets for
compounds from other areas of drug discovery. All compound related
proteins were searched against target proteins from ChEMBL v26(Anna et
al.) using BLASTP (E[?]1\selectlanguage{ngerman}×10\selectlanguage{english}\textsuperscript{-10}), and a total of 4,554
small molecules with recorded biological activities were identified. By
blasting against the ChEMBL(Anna et al.) databases, a total of 90 known
genes, which encode specific drug targets were identified. The
corresponding drugs (13 drugs used to treat humans with World Health
Organization (WHO) ATC code P02 `WHO anthelmintics' and 10 drugs from
DrugBank(S et al., 2017)) were further collated by searching DrugBank
databases and the literature (Supplementary Data 4a). Some of these
drugs have been proven to be effective against \emph{B. schroederi} ,
such as albendazole(Fu et al., 2011), mebendazole(Bourne, Cracknell, \&
Bacon, 2010), pyrantel(Xie et al.) and ivermectin(C. Wang et al., 2015).
Many existing anthelmintics are compromised by the increase of
resistance in roundworm populations(D. Li et al., 2015). In addition to
known drugs, we were committed to identifying new potential drug
targets. We focused on single protein ChEMBL targets that may be easier
to develop drugs against than protein complexes(International et al.,
2018). By blasting against target proteins (similarities \textgreater{}
80\%) in the single protein database ChEMBL, we identified 95 genes
encoding single proteins. Then we set a score of `0/1' considering six
main factors to evaluate the potential of the protein as a drug target
(see methods; Fig. 8). Finally, we located the position of all the drug
target encoding genes on superscaffolds (Fig. 8). Since the existing
Phase III and above drugs have greater potential for development into
new anthelmintics, we searched for commercially available compounds
against each target protein although these compounds were not originally
designed as anthelmintics. Among all the proteins, we found that three
target genes (\emph{cmd-1} , \emph{Ap2s1} ,\emph{HRAS} ) have available
compounds with Phase III/IV approvals (Supplementary Data 4b). These
potential drug targets and compounds will provide references for the
development of new anthelmintics.

\textbf{4 \textbar{} DISCUSSION}

\emph{B. schroederi} exhibits strong environmental adaptability and wide
distribution, and is a threat to the health of giant pandas (Zou et al.,
1998). The in-depth studies of \emph{B. schroederi} have been hampered
by the lack of a high-quality genome. The scaffold N50s of
published\emph{A. suum} , \emph{P. univalens} and \emph{T. canis}
genomes are 290 kb, 1,825 kb and 375 kb, respectively (Table 1). In this
study, we present the first chromosome-scale genome assembly of the
\emph{B. schroederi} with the scaffold N50 of 12.69 Mb, representing a
genome assembly with the best contiguity in Ascarididae. We envisage
that this genome will provide a valuable and useful genetic resource for
future research on roundworms, as well as drug development for
expulsion.

Roundworms have special characteristics that are different from
free-living nematodes reflecting the adaptation to the parasitic life.
Eggs of roundworms have a tough and elastic polysaccharide chitin shell,
which enables eggs to persist in the soil for up to ten years(FAIRBAIRN,
1970). We have observed a significant expansion of the chitin-binding
protein \emph{CPG-2} family in roundworm branches, which may be related
to the formation of the roundworm egg shell, thereby prolonging the
survival of roundworms even in a harsh environment. In addition, in the
parasitic stage, larvae enter the intestine, penetrate the intestinal
wall, migrate among tissues and organs(K. Kazacos \& W. M. Boyce, 1989),
molt and develop, finally return to the small intestine to develop into
adults and lay eggs again. Some genes potentially involved in tissue
invasion and immune evasion have been significantly expanded in
roundworms, including genes homologous to metallopeptidase and
serine/threonine-protein kinase, respectively. Previous studies have
shown that metallopeptidase(s) in the secretory products of astacins in
the nematode epidermis can digest collagen in host tissues, and thus be
involved in the migration of larvae in viscera(Hanns et al., 2011;
Williamson et al.).

Although the morphological characteristics of Ascarididae roundworms are
similar, the \emph{B. schroederi} still shows unique molecular
evolutionary traits. The giant panda has gradually evolved in response
to the bamboo diet during millions of years of evolution(Zhou, Hu, Yuan,
\& Wei, 1997). However, the giant panda has maintained the intestinal
structure of carnivores, with short and thick small intestines(Guibo
Yang, 1995). Due to the low digestibility of bamboo, giant pandas have
to ingest large amounts of bamboo to meet their nutrient needs(Sims et
al., 2007), and a large amount of feces is produced and discharged every
day. Accordingly, \emph{B. schroederi} needs to absorb nutrients as much
as possible. In the \emph{B. schroederi} genome, several unique gene
families of \emph{B. schroederi} were found to be involved in the
metabolism of essential amino acids, especially the degradation of
valine, leucine and isoleucine (KO00280; \emph{P} \textless{}0.01),
which is likely to enhance the ability of \emph{B. schroederi} to absorb
nutrients. In addition, \emph{B. schroederi} needs stronger motor
ability than \emph{A. suum} , \emph{P. univalens} and \emph{T. canis} to
survive in the small intestine because of its much smaller body size,
and at the same time ensure that they can avoid expulsion. The muscle
tissue of the roundworm plays a key role in motility and the extreme
expansion and positive selection of the actin family may have provided
the driving force for muscle contraction and cell movement(Hall, 1998).
Actin promotes muscle contraction and plays a very important role in the
movement and migration of \emph{B. schroederi} in the host. Studies have
shown that actin is involved in the repair of nematode epidermis
damage(Suhong \& Chisholm, 2012), which is of great significance to the
migration of \emph{B. schroederi} in the giant panda. The expansion of
the actin gene family may, at least to some extent, explain the genetic
basis of stronger locomotion ability of \emph{B. schroederi} than other
roundworms.

According to a previous investigation, the cause of death of giant
pandas in recent decades has shifted from starvation and poaching to
VLM-related deaths(J.-S. Zhang et al., 2008). Frequent use of drugs may
drive the increasing frequency of genes related to drug resistance in
the population, leading to widespread drug resistance in the \emph{B.
schroederi} population. Furthermore, there have been reports of side
effects in giant pandas after the administration of existing
anthelmintic drugs(C. Wang et al., 2015). We identified the \emph{ABC} ,
\emph{CYP} , \emph{GST} , \emph{CHIA} ,\emph{PTCHD} and \emph{PTP} gene
families in the \emph{B. schroederi}genome. These genes may be involved
in the metabolism of drugs and other xenobiotics and/or biosynthesis and
metabolism of endogenous compounds. We observed a recent significant
positive selection of \emph{ABC} and\emph{CYP} family members and other
resistance-related genes (\emph{glc-1} , \emph{nrf-6} , \emph{pgp-3} and
\emph{bre-4} ) in captive (SC) populations. Although wild and captive
populations were obtained from two different regions (Qinling and
Sichuan), natural selection analysis mainly considers recent changes in
gene frequency. The two populations are facing completely different
selection pressures for deworming, and thus, offer an option for
evaluating natural selection trends of a few resistance-related genes.
The results indicated an increased frequency of drug resistance-related
genes in captive populations. This may be related to the frequent use of
drugs in recent decades. Although the degree of natural selection in the
current resistance areas cannot be quantified, it is possible that the
gene frequency of these genes is still increasing, and it may cause the
emergence and increase of resistant individuals. Studies have shown that
some new sources of infection may even evolve into potential
antibiotic-resistant pathogens(Zumla \& Hui, 2019). Therefore, the
identification of drug-resistance genes and the detection of
drug-resistant individuals are still essential in future works.

There is an urgent need for new anthelmintic drugs for intestinal
expulsion of roundworms(James, Hudson, \& Davey, 2009; Jia, Melville,
Utzinger, King, \& Zhou, 2012). Specifically, there is a pressing need
for new anthelmintic drugs to protect the giant panda, since existing
drugs suffer from low efficacy, serious side effects or rising drug
resistance in parasite populations due to increased frequency of use(C.
Wang et al., 2015) . The chromosome-scale genome of \emph{B.
schroederi}provides a reference for the development of species-specific
drugs, and drug targets can be screened from the whole genome level. We
identified a total of 90 known drug targets and 95 potential drug
targets, providing a basis for the development of follow-up drugs and
vaccines. We searched four compounds (lonafarnib, haloperidol,
trifluoperazine and chlorpromazine; Supplementary Data 4b) that have a
phase 3/4 approval. These compounds could be considered for repurposing
as novel anthelmintics, which would save considerable effort and
expense. Nevertheless, the anthelmintic activity of these compounds and
other potential target compounds needs further testing. We envision that
such works will provide new modalities for the prevention and treatment
of baylisascariasis and other parasitic diseases.

\textbf{5 \textbar{} CONCLUSIONS}

Roundworms has undergone significant differentiation and host
specificity in the process of accompanying host evolution. This can be
clearly seen in the evidence in morphology and molecular biology. The
roundworm genome provides the possibility to study the details of gene
selection or loss in the process of roundworm differentiation. It is
also the case that some of the genes we have identified have potentially
allowed new avenues for gene selection and intestinal environment
adaption, for example, epidermal chitin synthesis, environmental
information and essential amino acid metabolism. In addition, population
genomics analysis and drug prediction provide insights for revealing the
impact of deworming history on population genetic structure and
prevention.

\textbf{ACKNOWLEDGMENT}

This work was supported by the National Key R\&D Program (No.
2017YFD0501702), Open Project of Key Laboratory of SFGA on Conservation
Biology of Rare Animals in The Giant Panda National Park (CCRCGP,
No.2020004), Forestry science and technology research project (No.
20180302), the Pearl River Talent Recruitment Program in Guangdong
Province (2019CX01N111), Fundamental Research Funds for the Central
Universities (No. 2572020AA30) of China, the Foundation of Key
Laboratory of State Forestry and Grassland Administration (State Park
Administration) on Conservation Biology of Rare Animals in the Giant
Panda National Park (No. KLSFGAGP2020.002), and the Guangdong Provincial
Key Laboratory of Genome Read and Write (grant No. 2017B030301011).

\textbf{AUTHOR CONTRIBUTION}

Z.J.H., and H.L. designed and initiated the project. Z.W.P., D.S.L.,
S.W.H., Y.L.W., H.T.S., H.L.C., and L.H.D. collected the samples.
X.F.C., L.H., L.L.L., Y.X.L., Y.Q.Z., J.Y.Y. and H.R.L. performed the
DNA extraction, library construction and sequencing. L.H., T.M.L.,
H.M.L., R.B.H., Q.W., Y.X.Z., and S.C.Y. performed the data analysis.
L.H., T.M.L. and wrote the manuscript. Z.J.H., K.K., M.L., D.S.L., H.L.,
Q.L., H.M.Y. and X.X. supervised the manuscript. All authors read and
approved the final manuscript. L.H. and T.M.L. wrote the manuscript with
input from Z.J.H., K.K., M.L., D.S.L., H.L., Q.L., H.M.Y. and X.X.
Z.J.H., H.L., K.K. and Q.L. supervised the project.

\textbf{CONFLICT OF INTERESTS}

The authors declare no conflict financial interests.

\textbf{DATA AVAILABILITY}

The data that support the findings of this study have been deposited
into CNGB Sequence Archive (CNSA)113 of China National GeneBank DataBase
(CNGBdb)114 with CNSA project ID CNP0001242.

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\textbf{Tables}

Table 1. Summary of the features of the\emph{B. schroederi} genome.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Description & \emph{B. schroederi} (this study) & \emph{B.
schroederi}\textsuperscript{\#} & \emph{A.suum} & \emph{P. univalens} &
\emph{T. canis}\tabularnewline
\midrule
\endhead
Genome size (bp) & 262,743,947 & 281,639,769 & 265,545,801 & 253,353,821
& 317,115,901\tabularnewline
Number of Scaffolds; Contigs & 106; 639 & 2,778; 15,567
(\textgreater{}1000 bp) & 31,538; 40,509 & 1,274 & 22,857;
51,969\tabularnewline
Average length of Scaffolds; Contigs (bp) & 2,478,717; 410,763 & - &
8,420; 6,506 & 198,864; 6,918 & 13,874; 5,747\tabularnewline
Gap length (bp; \% of genome) & 266,500 (0.10\%) & 13,257,555 (4.70\%) &
1,980,846 (0.7\%) & 966,507 (0.38\%) & 18,474,532
(5.82\%)\tabularnewline
N50 of Scaffolds; Contigs (bp) & 12,688,039; 1,154,808 & 888,870; 42,126
& 290,558; 46,632 & 1,825,986; 20,376 & 375,067; 16,980\tabularnewline
N90 of Scaffolds; Contigs (bp) & 7,262,801; 161,963 & 104,281; 7439 &
48,674; 10,466 & 204,976; 2,991 & 66,363; 3,511\tabularnewline
Genome GC content (\%) & 37.34 & 37.26 & 37.85 & 39.07 &
39.95\tabularnewline
Repetitive sequences (\%) & 9.53 & - & 4.4 & - & 13.5\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{\#} The published genome information of \emph{B.
schroederi} (not released)(Y. Hu et al., 2020).

\textbf{FIGURE LEGENDS}

\textbf{Fig.1 The phylogenetic relationships among six nematodes and
genomic characteristics and synteny of \emph{B. schroederi}.(a)} Genomic
characterization of \emph{B. schroederi} genome. The figure shows the
gene number, repeat content, GC content, sequencing coverage and
scaffolds from the center to the edge. \textbf{(b)} Synteny of \emph{B.
schroederi} with \emph{P. univalens} and \emph{T. canis} at the gene
level. Different colors represent different synteny blocks.\textbf{(c)}
Upset plot showing the intersection of gene family expansions in
nematodes. Each row represents a nematode. Black circles and vertical
lines between the rows represent the intersection of expanded families
between species. The barplot indicates the total gene family count in
each intersection. \textbf{(d)} Time-calibrated maximum likelihood
phylogenetic tree of six nematodes. The estimated divergent times are
shown at the bifurcations. The numbers below the nodes represent the
number of gene families significantly expanded, maintained, and
contracted, respectively.

\textbf{Fig.2 The expansion and contraction of roundworm gene
families.(a)} Significant increases and decreases in roundworm gene
families. The solid circle and the solid triangle represent the top KEGG
pathways that are enriched in the expanded gene families of Ascariasis
compared with \emph{C. elegans} or \emph{M. hapla} , respectively. The
open circle and the open triangle represent the top KEGG pathways that
are enriched in the contracted gene families of Ascariasis compared
with\emph{C. elegans} or \emph{M. hapla} , respectively. \textbf{(b)} GO
function enrichment and gene copy number of the significantly expanded
gene families in roundworms; \textbf{(c)} The proportion of GO
functional genes in the gene family with significant expansion (or
contraction) in roundworms compared to the total number of expansion (or
contraction) genes. The red asterisk represents the p value of
statistical Sidak's multiple comparisons tests of expansion and
contraction of genes comparing with \emph{C. elegans} or \emph{M.
hapla}(One asterisk represent 10\textsuperscript{-1}).

\textbf{Fig.3 Expansion and contraction of}\emph{B. schroederi}
\textbf{gene families compared with three roundworms (\emph{A. suum}
\emph{P. univalens} and \emph{T. canis}). (a)} Enrichment of the KEGG
pathway in some significantly expanded gene families of \emph{B.
schroederi} . The proportion represents the ratio of the number of
expanded genes located in the pathway (target genes) to all genes in the
pathway (background genes). \textbf{(b)} REVIGO clusters of
significantly over-represented GO items for significantly expanded gene
families in \emph{B. schroederi} . The position of the bubbles is based
on semantic similarity of GO terms. \textbf{(c)} Enrichment of KEGG
pathways in \emph{B. schroederi} 's unique gene families. \textbf{(d)}
Heatmap showing the gene families of \emph{B. schroederi} that are
significantly expanded or contracted. The x axis represents the four
roundworms of Ascariasis, whereas the y axis represents the families.

\textbf{Fig.4 Life history of \emph{B. schroederi} and the effect of
actin gene on muscle contraction. (a)} Life history of \emph{B.
schroederi} . L1 and L2 represent in vitro developmental stages, and L2
larvae enter the host body after developing into the infective stage. L3
and L4 represent the stage of internal organ migration of the larva.
Stage L5 larvae return to the small intestine and develop into adult
worms through sexual maturation. \textbf{(b)} Schematic diagram of
anatomical cross-section of \emph{B. schroederi} ; \textbf{(c)} Multiple
signaling pathways are involved in actin polymerization, and genes in
red are positively selected genes (PSGs). It shows significant expansion
of three key gene families involved in actin polymerization.

\textbf{Fig.5 Demographic history of the}\emph{B. schroederi}
\textbf{reconstructed from the reference and population resequencing
genomes. (a)} The red and purple line represent the estimated effective
population size of \emph{B. schroederi} and host, respectively. The 100
gray curves of \emph{B. schroederi} and host represent the PSMC
estimates for 100 sequences randomly resampled from the original
sequence\textbf{.} Generation time (g) of \emph{e} and giant panda were
0.17 and 12 years, respectively. The neutral mutation rate per
generation (\selectlanguage{greek}\emph{µ} \selectlanguage{english}) of \emph{B. schroederi} and giant panda were 0.9
\selectlanguage{ngerman}× 10\selectlanguage{english}\textsuperscript{-8} and 1.3 x 10\selectlanguage{english}\textsuperscript{-8}, respectively.
The black line shows the MAR of Chinese loess. (\textbf{b)} Longitudinal
change of the effective population size of the \emph{B.
schroederi}populations. The effective population sizes (\emph{Ne} ) were
estimated using the MSMC2 method. QLI: Qinling population; SC: Sichuan
population.

\textbf{Fig. 6 Population structure and relationships of Sichuan (SC) in
comparison to Qinling (QLI) population. (a)} The geographic distribution
of the sampling locations for QLI and SC populations. \textbf{(b)} PCA
analysis of two populations; \textbf{(c)} A maximum likelihood (ML)
phylogenetic tree with 100 bootstrap tests constructed using
whole-genome SNPs information. We used \emph{P. univale} as the
outgroup; \textbf{(d)} Population structure of SC and QLI populations (K
from 2 to 5). The y axis quantifies the proportion of the individual's
genome from inferred ancestral population, and x axis shows the
different individuals.

\textbf{Fig.7 Analysis of natural selection in captive populations.
(a)}Genomic regions with selection sweep signals in captive (SC) and
wild (QLI) \emph{B. schroederi} population. Distribution of ln ratio
(\selectlanguage{greek}θ\selectlanguage{english}\selectlanguage{greek}\textsubscript{π, wild(QLI)}\selectlanguage{english}/\selectlanguage{greek}θ\selectlanguage{english}\selectlanguage{greek}\textsubscript{π, captive(SC)}\selectlanguage{english})
and\emph{Fst} of 50 kb windows with 10 kb steps. Red dots represent
windows fulfilling the selected regions requirement (corresponding to Z
test P \textless{} 0.005, where \emph{Fst} \textgreater{}0.21 and ln
ratio \textgreater{}0.34). \textbf{(b)} Plot of iHS showing loci under
positive selection of captive (SC) population. SNPs with
\textbar{}iHS\textbar{}[?]iHS\textsubscript{m} (3.89, top 1\%) were shown
above the dashed horizontal line. Nucleotide diversity
around\emph{glc-1} , \emph{nrf-6} , ABC transporter \emph{ced7} and
\emph{bre-4}loci using 10-kb sliding windows were displayed above the
genes. The decay of haplotype homozygosity around a focal marker were
displayed on the right side of the figure. The furcation structures
represent the complete information contained in the concept of extended
shared haplotypes EHH(Pardis C. Sabeti et al., 2002). The root (focal
marker) is indicated by a vertical dashed line. The thickness of the
lines corresponds to the number of scaffolds sharing a haplotype.
\textbf{(c)}XP-EHH from each SNP core showing the same nucleotide
between the subject and the comparison target, also transformed to
\emph{P} values and plotted in logarithmic scale.

\textbf{Fig.8 The position of known and potential drug target genes
onsuperscaffolds.} Different colors indicate different known drugs, and
black indicates potential drug targets. The chemical structural formulas
of 23 known drugs are drawn. The circles following the potential drug
targets represent the six criteria, with a red solid circle indicating
match condition and a hollow circle indicating mismatch condition. the
six criteria were: (1) Similarity with ChEMBL known drug targets having
a highly conserved alignment (\textgreater{}80\%); (2) Lack of human
homologues; (3) Related to lethal, L3 arrest, flaccid, molt defect or
sterile phenotype; (4) A predicted metabolic chokepoint; (5) A predicted
excretory/secretory protein (EP); (6) The protein has a structure in the
PDBe. Potential drug target proteins encoding genes on each
superscaffold and corresponding scores are marked (black).\selectlanguage{english}
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