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Background

Tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) is still one of the major global public health problems.1,2 There were approximately 842,000 new TB patients in China in 2020, and the estimated incidence rate was 59/100,000.3 Children and adolescent populations are often overlooked in terms of TB incidence and their access to TB care. In 2019, 396,000 children and adolescents aged 1019 years were reported with TB, accounting for 10% of total notifications in 95 countries globally.4 TB outbreaks in schools have been reported in other countries, such as Japan, Mongolia, Korea, Serbia, and Swaziland.58 Despite differences in TB burden and outbreak preparedness, the affected schools were confronted with similar challenges including delayed diagnosis of index cases, lack of experienced medical personnel, a lack of sustained financial support, and difficulty in responding to media and community attention.6

It has been increasingly recognized that adolescents and school-aged people are vulnerable to TB infection.11 A study showed that there were 39,198 TB patients among students in China, accounting for 4.12% of the total TB burden, and the most common outbreak sites were high schools.12 TB is easily spread within schools due to the large population density, close contact, and poor ventilation in classrooms and dormitories.13 The outbreak of TB in schools affects the students health and can even cause major public health events.13 However, despite the shared common epidemiological links for TB patients in school outbreaks, it is unclear whether these TB patients were direct transmission events in school or acquired from the community outside the school.

Recently, whole-genome sequencing (WGS) has played a vital role in transmission inference9,14 of M. tuberculosis. Since the traditional epidemiological investigation heavily relies on the disease history of close contact, it is difficult to clarify the transmission chain of TB due to the natural history of TB disease. WGS is considered an ultimate genotyping tool for TB outbreak investigations, which provides a high resolution in determining clusters for transmission analysis compared to conventional methods such as mycobacterial interspersed repetitive unit-variable number tandem repeat typing (MIRU-VNTR).15,16 Recent studies have shown that WGS was applied in TB outbreaks to find the source and determine the transmission route in communities in China.17,18

Hunan Province, located in south-central China, is one of the provinces with a high TB burden in China,9 with an estimated notification of a TB incidence rate of 74 patients per 100,000 population. From 2012 to 2017, 7940 students with TB were notified in Hunan Province, with a registered incidence rate of 13.2 per 100,000 population, suggesting the relative high school tuberculosis epidemic of Hunan Province.10 Here, we reported the epidemiology study of three TB outbreaks in schools of Hunan Province in 20172019, using WGS analysis combined with the field epidemiological investigation to better understand the transmission characteristics and main influence factors of these outbreaks, thereby providing evidence for the prevention and control of TB epidemics in schools.

According to the diagnostic criteria of pulmonary tuberculosis issued by the National Health and Family Planning Commission of China,19 the diagnosis of pulmonary tuberculosis is mainly based on etiological (including bacteriology and molecular biology) examination, combined with epidemiological history, clinical symptoms, chest radiologic evidence, and related auxiliary examinations. In this study, the three TB outbreaks occurred in three high schools in the south, central and north of Hunan Province from 2017 to 2019. When we received a report of a student TB patient (index case), we immediately carried out a field epidemiological investigation in the school. We investigated the index case to identify the close contacts, and then conducted TB screening for all contacts. Once an additional new TB patient was identified, the field investigation would expand to the whole floor or school. We further conducted WGS to elucidate the transmission patterns of these outbreaks.

An epidemiological investigation was conducted among the index case and their close contacts. The index case was defined as the first identified TB patient in each school.17 Close contacts refer to those who have direct contact with index cases, and the first-round screening of close contacts mainly included teachers and students in the same class and dormitory. Systematic TB screening was performed by clinical evaluation, tuberculin skin testing (TST), chest radiography (CXR), and laboratory test for all close contacts. The laboratory test included sputum smear and culture, drug susceptibility test was further conducted if culture positive.20 If one or more patients of TB were newly discovered in the contact screening, the second round of screening of the close contacts would be expanded to the floor and/or the building of the class and/or dormitory through the school.20 In addition, the family member contacts would be referred to the local hospital for screening. Each TB patient was interviewed using a standardized questionnaire by the local Centers for Disease Control and Prevention (CDC) staff (Additional file 1). The questionnaire included individual information, medical history, exposure history, symptoms of the first onset of illness (cough, expectoration, hemoptysis, low fever, night sweats, fatigue, etc.), the reasons for patient delay and healthcare diagnostic delay, and clinical treatment history. The patient was judged to have a patient delay if the interval between the date of the first symptom and the first visit (doctor) exceeds two weeks.21 Similarly, healthcare diagnostic delay is defined as more than two weeks between the date of the first health facility visit and the confirmation of a TB diagnosis.22

Sputum specimens were collected from all the patients for the investigation, including routine sputum smears, bacterial isolation, and culture. The M. tuberculosis isolates used for WGS were obtained from 15 culture-positive TB patients of the three schools, including one strain of a patients family member. The standard strain H37Rv was provided by the Tuberculosis Laboratory of the National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention. The number of culture-positive isolates from each school was as follows: School A, 4; School B, 6; School C, 5.

The sputa were processed by the standard N-acetyl L-cysteine (NALC)/NaOH decontamination method. The decontaminated sediments were resuspended in 2.0 mL of phosphate-buffered saline and 0.5 mL was inoculated into Bactec MGIT 960 tubes. Tubes flagged positively by the MGIT 960 instrument were subjected to further M. tuberculosis complex identification through the commercial MPT64 immunochromatographic test (GENESIS, Hangzhou, China).

Drug susceptibility tests (DSTs) for Rifampin (RIF) and Isoniazid (INH) resistance were performed according to the manufacturers instructions (Becton, Dickinson and Company, Sparks, MD) with the following drug concentrations: RIF 1.0 g/mL and INH 0.1 g/mL. The in-silico drug resistance was predicted using TB-Profiler v2.8.14 and based on genomic sequencing data, including RIF, INH, Amikacin (AMK), Capreomycin (CPM), Ciprofloxacin (CIP), Ethambutol (EMB), Kanamycin (KM), Moxifloxacin (MFX), Ofloxacin (OFLX), Pyrazinamide (PZA), and Streptomycin (SM), Bedaquilin (BDQ) and Delamanid.

Genomic DNA of M. tuberculosis isolates was extracted using the CTAB method for sequencing as previously described.23,24 DNA libraries were constructed with genomic DNA using kits provided by Illumina according to the manufacturers instructions. The average sequencing depth of the genome is 124.8, and the coverage is 99.3%. DNA libraries were then selected to perform cluster growth and 150 bp paired-end sequencing on an Illumina HiSeq 2500 according to standard protocols. Paired-end reads were mapped to the reference genome H37Rv (GenBank accession number, NC_000962.3) with Bowtie2. The SAMtools (version 1.6) and VarScan (version 2.3.9) suite were used to define SNPs, with low-quality SNPs (Phred score Q < 20 and read depth < 5) and sites with missing calls in > 10% of isolates were removed. Heterogenous sites were called the consensus allele if present in 80% of mapped reads. SNPs in repetitive regions, PE/PPE genes and in resistance-conferring genes were excluded from further phylogenetic analysis.

The alignment of concatenated SNPs of the 15 strains was used to construct a maximum-likelihood (M-L) phylogenetic tree with Mega X,25,26 using the GTR nucleotide substitution model with 500 bootstrap samples. We compared the pairwise genomic distance and defined a genomic cluster as the genetic distance of strains that were no more than 12 SNPs.27,28

Ethical approval was obtained from the Research Ethics Committee of the Hunan Provincial Chest Hospital (protocol number: KLS2019092501). The informed consent form was obtained from all study participants aged 18 years or their parents or legal guardians if < 18 years before enrollment, and ethical principles conformed to the Declaration of Helsinki.

Statistical analyses were performed using the SPSS statistical package (version 21.0). Continuous variables are reported as the mean standard deviation, and nonnormally distributed variables are reported as the median (quartile range). Categorical variables are shown as the frequency (%). Categorical variables were compared by the 2 test or Fishers exact test as appropriate. P < 0.05 was regarded as statistically significant.

In December 2017, a 16-year-old male student in School A went to the countys central hospital because of cough and expectoration for two weeks. He was diagnosed as a rifampin-resistant patient, and received standard treatment at Hunan Provincial Chest Hospital. The sputum smear was negative, but the sputum culture was positive. The time interval from the first symptom to definitive diagnosis was 46 days, and the reason he self-reported for the delay in seeking medical treatment was the stigma of TB and to conceal the disease (he suspected he had TB before visiting doctors). We also found his mother was an MDR patient, indicating a potential household transmission.

In February 2019, a 16-year-old male student in the 11th grade of School B was reported to have TB. He had symptoms of cough, expectoration, low fever, and fatigue for weeks before the TB diagnosis. At that time, he made a common neglect for consideration of a cold and received no treatment because of the lacking knowledge of TB. On February 25, because of the unrelieved symptoms, he went to the hospital to seek further care and was diagnosed with TB followed by standardized anti-TB treatment. The sputum smear was negative but the sputum culture was positive, and small cavities were found in his lung. The time interval from the first symptom to definitive diagnosis was 37 days.

In February 2018, a 17-year-old male student in School C was diagnosed with TB. Because of the lack of the knowledge of TB symptom, he did not pay enough attention to the repeated expectoration and cough since July 2017. Until February 6, 2018, he went to the emergency department of the county hospital for hemoptysis caused by a collision with the chest and was diagnosed with TB. Then he underwent standardized anti-TB treatment in the hospital. Small cavities were found in his lung with sputum smear-negative and sputum culture-negative. The time interval from the first symptom to definitive diagnosis was 247 days.

A total of 6569 close contacts of the index cases were identified, including 418 from School A, 4020 from School B, and 2131 from School C. In the first-round screening for close contacts, 31 TB patients were identified. In addition to the index case, there were 8 (13.8%, 8/58), 5 (7.6%, 5/66), and 21 (40.4%, 21/52) patients in the class of index cases in School A, School B, and School C. In the second round of screening (except for the first round screened), a total of 15 TB patients were identified, including 1 (0.3%, 1/361) in School A, 9 (0.2%, 9/3955) in School B, and 5 (0.2%, 5/2080) in School C, respectively. In total, 49 new TB patients were identified during the two rounds of TB screening among the school population, including 20 pathogen-positive patients (with microbiological findings) and 29 pathogen-negative patients. The survey procedure is shown in Figure 1. The putative attack rates of the classes and schools of index cases are shown for each outbreak in Table 1. There was a significant difference in the putative attack rates between the class and the school (Tables 1 and 2).

Table 1 The Status of Contact Survey of Tuberculosis Outbreaks in Three Schools

Table 2 Demographics and Clinical Characteristics of the TB Cases (N = 49)

Figure 1 Diagram of the close contact survey process. Laboratory test: sputum smear and culture for suspects, DST also conducted if culture positive.

This investigation identified a total of 49 TB patients in the three schools, with which three school TB outbreaks were identified. There were 36 (73.5%) male and 13 (26.5%) female patients. Except for one patient who was a 31-year-old female teacher, the others were all students with a median age of 17 years (IQR 1718). Most TB patients (34, 69.4%) were in the 12th grade. Thirty-four patients (69.4%) were in the same class as the index cases, and 20 (40.8%) patients were bacterially positive. The epidemiological characteristics of the 49 patients diagnosed with TB are shown in Table 2.

The symptom distributions of TB patients are shown in Figure 2. Among 49 patients, 28 (57.1%) had TB-like symptoms and the others showed no related symptoms. Among these 28 patients, the most common symptoms were cough and expectoration, of which the incidence was 85.7% and 53.6%, respectively, and the following symptoms, such as low fever, fatigue and night sweats, were 10.7%, 10.7% and 7.1%, respectively.

Figure 2 The first symptom proportion of the patients.

A total of 13 patients had patient delay, with a median delay interval of 69 days (IQR 30.5113 days). Among them, eight patients had a patient delay because they were unaware of the connection between the symptoms of cough or expectoration with TB. A total of 12 patients had a healthcare diagnostic delay, with a median delay interval of 32 days (IQR 2482 days). The main reasons for these delays were that the patients were misdiagnosed with bronchitis and other non-tuberculosis diseases during healthcare seeking.

Four isolates from School A had phenotypic rifampin-resistance, and two of these isolates were also isoniazid-resistant (ie, multidrug-resistant tuberculosis, MDR-TB). The patients in School B and School C were all phenotypically susceptible to rifampin and isoniazid. Based on WGS-predicted genetic drug resistance profiles, the isolates from School B and School C were pan-susceptible. MDR-TB and rifampin-resistant tuberculosis (RR-TB) were identified among three isolates from patients in School A, involving two students and one family member. We further identified that at least three patients in school A had strains harboring the second-line injectable anti-TB drugs (rrs, A1401X) (Figure 3 and Additional file 2), indicating the direct transmission of drug resistant TB both in households and schools. The comparisons between the DST and in-silico predicted drug resistance was shown in Additional file 3.

Figure 3 Maximum-likelihood tree of 15 strains annotated with epidemiological characteristics related to drug resistance. Branches are colored by different schools. MDR, rifampicin and isoniazid resistant. INH-mono, rifampicin susceptible and isoniazid resistant. Pan-susceptible, rifampicin and isoniazid susceptible. Patients A0 and B0 were index cases.

We conducted genomic and phylogenetic analyses based on the M. tuberculosis strains collected from 15 culture-positive patients from the three schools. All these M. tuberculosis strains were identified as the sublineage L2.2.1 (subgroup of the Beijing family strain), except one strain belonging to lineage 4.1 (Figure 3).

Aligning reads against the reference strain revealed 1803 SNPs that were used to reconstruct an M-L phylogeny tree. The M-L clearly showed that each school formed separate clusters, while all the pairwise genetic distances within each cluster were less than ten SNPs, with a median distance of four SNPs, indicating the recent transmission and spread of these outbreaks in a short period. However, not all the new patients notified during expanded screening belonged to the outbreak chain, as one TB patient had an isolate with more than 100 SNPs from the other strains in the same school (School A), which was unlikely to belong to the same transmission chain and suggested a different route of infection. The pairwise SNP distances between 15 isolates and H37Rv are shown in Table 3. The cluster of School A involved a mother and her son, indicating both household and school transmission events of MDR-TB strains. The strains from six students in School B and five in School C formed two separated clusters (Figure 3 and Additional File 4). Based on the SNP distance matrix and reconstructed phylogeny trees, we confirmed that these three school TB outbreaks were independent events (i.e., SNP distance >100 between any cases from two schools, Table 3 and Additional File 4). Overall, the WGS analysis confirmed the TB outbreak among these three schools and differentiated transmission events from non-school sources, which were more likely from the household.

Table 3 The Pairwise SNP Distance Matrix of the 15 School TB Isolates and Reference Strain

The current study reported three outbreaks of TB in high schools in Hunan Province, China, which were confirmed by epidemiological investigation and WGS analysis. A total of 6569 students and staff in schools were screened by TST and CXR examinations, and 49 TB patients were identified by laboratory confirmation and/or clinical diagnosis, including one teacher and 48 students. Whole-genome sequencing analysis confirmed the recent transmission of TB among students and teachers, but also revealed that not all of the screened new TB patients were involved in the school transmission chain. Instead, the reason might be singletons and probably resulted from the infection events outside the school.

Students in high school are one of the vulnerable and high-risk population groups to TB in which it is easy to develop and spread infectious diseases and even cause outbreaks. Reports of TB outbreaks in schools have increased recently in China, including a previous report of a TB outbreak of 90 students in a high school in the same province.7,2931 In 2017, the National Health and Family Planning Commission and the Ministry of Education of China released a guideline to strengthen the early diagnosis and treatment of pulmonary TB in all levels of schools.20 High school education is the most important stage as students need to take college entrance examinations.32 They usually face lots of pressure to study and reduce time in exercise, which could lead to weakened immunity.33 Furthermore, they often attach little importance to upper respiratory tract infection symptoms, such as cough and expectoration, causing a patient delay. All the index cases experienced a long diagnosis delay, and the time intervals of patient delay were also relatively long in the three schools (69 days, IQR 30.5113 days). In general, the student TB patients (both index cases and secondary patients) were more likely to overlook TB-like symptoms such as cough and expectoration. Another explanation for diagnosis delay could be the misdiagnosis of non-TB diseases such as bronchitis. For example, the TB outbreak in School C was caused for this reason. In summary, reinforcing routine education regarding TB control knowledge is a necessary step for high schools in China.

We conducted a drug susceptibility assay on all collected isolates in the three schools and found that only isolates from School A showed resistance to rifampin, isoniazid, and second-line injectable anti-TB drugs. We found the index patient of School A with a previous history of TB, and his mother was also an MDR-TB patient. Therefore, the index patient might have been infected with M. tuberculosis by his mother and caused the school transmission, which was verified by WGS. The hide of disease of the student contributed to the spread of drug-resistant TB in School A.

WGS has shown a high resolution in determining TB transmission dynamics in many settings worldwide.27,34,35 WGS analysis has advanced power in measuring genetic relatedness between strains compared to other PCR-based genotyping tools, which have been approved to study TB transmission in strains circulating in China.27,36 The ability to determine the transmission relationship is important in TB outbreaks in schools since the outbreak occurrences of infectious disease in the student population are always in high tension. Furthermore, the utility of the WGS tool can differentiate unrelated transmission from school outbreaks despite common epidemiological links (e.g., same class or dormitory). A study in Guangxi Province, China, also showed that previous exposure to TB in the household led a student to catch TB at school, and then spread it to his classmates.37 It emphasized that the source of school TB outbreaks might not be in the school but be infected outside the school. Again, this finding highlighted the importance of improving the identification of TB among adolescents. The application of WGS has gained insight into the investigation of TB outbreaks in schools and confirmed transmission inference with traditional epidemiological investigation.

There were several limitations in our study. First, not all the TB patients had culture-positive isolates. The small number of M. tuberculosis isolates from TB patients limited the role of WGS in transmission inference, including the transmission chain analysis. We also cannot fully rule out the possibility of recent transmission of the singleton strain in our study. Second, the studys relatively short duration limited the identification of more TB patients that might involve in the transmission chain but had not developed TB based on the nature of the disease. Despite these limitations, our study highlighted the critical role of epidemiological investigation and WGS in the transmission of school TB.

In summary, this study combined the field investigation and the WGS analysis and revealed at least three independent TB outbreaks in the schools. Patients diagnosis delay mainly contributed to the recent transmission of TB in this vulnerable population. Furthermore, the identification of the MDR-TB cluster involving family and school links reinforced the importance of TB control in both general and school populations. The TB outbreaks among senior high school students also raise more concern on the awareness of TB-like symptoms during routine healthcare activities, which can benefit TB control in the early stage of the epidemic and prevent the further spread in school populations.

TB, tuberculosis; TST, tuberculin skin testing; CXR, chest X-ray; WGS, whole-genome sequencing; M. Tuberculosis, Mycobacterium tuberculosis; MIRU-VNTR, mycobacterial interspersed repetitive unit-variable number tandem repeat typing; CDC, Centers for Disease Control and Prevention; DST, drug susceptibility tests; RIF, rifampin; INH, isoniazid; AMK, amikacin; CPM, capreomycin; CIP, ciprofloxacin; EMB, ethambutol; KM, kanamycin; MFX, moxifloxacin; OFLX, ofloxacin; PZA, pyrazinamide; SM, streptomycin; BDQ, bedaquilin; IQR, interquartile range; M-L, maximum-likelihood; MDR-TB, multidrug-resistant tuberculosis; RR-TB, rifampin-resistant tuberculosis.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

We thank all the investigators from the study sites for their contribution.

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

This research was supported by the Natural Science Foundation of Hunan Province, China (grant number: 2018JJ2215).

The authors declare that there are no conflicts of interest.

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13. Bao H, Liu K, Wu Z, et al. Tuberculosis outbreaks among students in mainland China: a systematic review and meta-analysis. BMC Infect Dis. 2019;19(1):972. doi:10.1186/s12879-019-4573-3

14. Merker M, Egbe NF, Ngangue YR, et al. Transmission patterns of rifampicin resistant Mycobacterium tuberculosis complex strains in Cameroon: a genomic epidemiological study. BMC Infect Dis. 2021;21(1):891. doi:10.1186/s12879-021-06593-8

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Whole-Genome Sequencing and Epidemiological Investigation of Tuberculo | IDR - Dove Medical Press

Diagnostics and research startup MedGenome announced on Tuesday it raised $50 million led by life science-focused Novo Holdings, bringing total funding to $185.5 million.

MedGenome, a California-based startup, leverages genomic sequencing platforms to aid in diagnostics and drug discovery.

Most notably, the 9-year-old startup also collects samples from patients in and around the Indian subcontinent to better map out variations in genetic sequencing among the South Asian population. Leveraging a network of over 4,000 hospitals and 10,000 doctors around the world, MedGenome has distributed over 300,000 genetic tests. The company says it has built the largest database of South Asian genetic variants.

Grow your revenue with all-in-one prospecting solutions powered by the leader in private-company data.

The fresh funding will launch the company out of South Asia and into Africa and the Middle East.

Breakthroughs and discovery are only as successful as the data on which theyre based, said Dr. Felix Olale, global co-lead for health care investments at LeapFrog Investments in a statement, MedGenomes mission to expand the global genomic dataset to aid in the development of more inclusive and equitable research and drug discovery is not only inspiring, but critical to the future of global healthcare.

Scientists envision a genomic sequencing utopia where enough data exists to predict if an otherwise-healthy person is at risk for diseases, allowing patients to receive preventative care early on. Several countries leveraged genomic sequencing to map out COVID-19 outbreaks down to the very person that hosted a new variant.

But the vast majority of genetic testing happens in high-income countries such as those in Europe and North America, leaving a large slice of the population untested. This is dangerous: Any research or patterns derived from a Europe-heavy dataset skews what treatment looks like for everyone.

Genomic sequencing technology is what allowed scientists to create a vaccine against COVID-19 without ever having a sample of it. The technology partially led genomics startups to receive a record $2.3 million in venture funding in 2021, according to Crunchbase data.

Illustration: Dom Guzman

Stay up to date with recent funding rounds, acquisitions, and more with the Crunchbase Daily.

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MedGenome Raises $50M To Map The Human Genome - Crunchbase News

Recovering 32,277 microbial genomes from over 6000 early-life gut metagenomes

To elucidate differences in the early-life gut microbiome at the genome level and also to expand the genomes for novel human gut lineages during early life, we employed a combination of metagenomic assembly and binning on 6122 multi-country distributed metagenomes across four continents from children from birth to three years old (Fig.1a; Supplementary Data1). Compared to the metagenomes that were used to build the Unified Human Gastrointestinal Genome (UHGG)22, 1904 metagenomes overlapped. The MAGs were produced by three different binning tools (i.e., MetaBAT23, MaxBin24, and CONCOCT25), and then integrated and refined to remove duplicates and improve the quality of assembled genomes with metaWRAP26 (Fig.1b). Following this pipeline, a total of 42,054 MAGs were met or exceeded the medium-quality (50% completeness and <10% contamination) based on the Minimum information about a metagenome-assembled genome (MIMAG) standard27. In order to provide stricter genome quality control, we selected those genomes having completeness >50% and contamination <5% together with genome quality score (defined as completeness5contamination, QS)>50 and free of chimerism (passed by GUNC28), resulting in 32,277 MAGs for subsequent analyses, which we referred to as the ELGG catalog (Fig.1c, d; Supplementary Data2). The median size of the 32,277 MAGs was 2.59 megabases (Mb) (interquartile range, IQR=2.083.75 MB) with N50 values between 1.7 kilobases and 2.8Mb. Among the ELGG catalog, 25,303 MAGs (accounting for 78.4% of the total dataset) were >90% complete (IQR=97.399.7%) and <5% contaminated (IQR=0.001.04%), hereafter referred to as near-complete genomes. A subset of 4614 MAGs (18.2% of near-complete genomes) had 5S, 16S and 23S rRNA genes as well as at least 18 of the standard tRNAs, which can be classified as the high quality draft genomes based on the MIMAG standard27. The relatively low proportion of high quality recovered MAGs was comparable with previous large-scale studies of human gut MAGs13,22 due to the typical challenge in the MAGs assembled from metagenomes with short reads. The rest of the ELGG catalog consists of 6974 medium-quality MAGs (>50% completeness and <5% contamination) (Fig.1d). The other genome statistics (including contig number and N50, genome depth, and relative abundance) supported the consistent high quality of near-complete MAGs compared to medium-quality MAGs even when the latter were stratified based on the QS at the threshold of 75 (Fig.1c).

a The number and proportion of fecal metagenomes stratified by clinical features including age, gender, delivery mode, gestational age, and feeding patterns. b Overview of the computational pipeline to generate ELGG and ELGP catalogs. c Quality metrics across near-complete (n=25,303), medium with quality score (QS)>75 (n=2063) and medium with QS75 (n=4911) MAGs. CPM copies per million reads. Boxes show the interquartile range (IQR), with the horizonal line as the median, the whiskers indicating the range of the data (up to 1.5 IQR), and points beyond the whiskers as outliers. d Completeness and contamination scores for each of 32,277 genomes. QS=completeness5contamination.

In line with previous studies15,22, the ELGG catalog was further investigated at the level of strain heterogeneity per genome by using CMSeq15, which has been suggested to represent a useful measure to assess genome quality. We found that the median strain heterogeneity (proportion of polymorphic positions) of genomes from the ELGG catalog was 0.005% (IQR=0.0010.031%; Fig.1c), which is much lower than the UHGG catalog (0.06%) that included the human gut samples covering all ages22. The near-complete genomes displayed a lower level of strain heterogeneity compared to the medium-quality genomes from the ELGG catalog (Fig.1c).

To expand our understanding of the functions of early-life gut microbiome, the protein-coding sequences (CDS) for each of the 32,277 MAGs were predicted, resulting in a total of 86,678,654 genes. This accounted for 54.9% of all genes when taking the unbinned contigs from the 6122 metagenomic samples into account. After clustering the protein sequences at 95% amino acid identity, we obtained 4,036,936 protein clusters, forming the ELGP catalog. Rarefaction analysis indicated a saturation point was still not reached as the number of ELGP clusters steadily increased as a function of the number of MAGs included (Fig.2a), and this pattern was also observed with the inclusion of all contigs from 6122 samples (Supplementary Fig.1a), which was in line with pervious observations22,29. However, when removing protein clusters with one protein sequence, the number of protein clusters approached saturation (Fig.2a; Supplementary Fig.1a). This may suggest that although the microbial genes from children gut microbiome are still underestimated, the majority of undiscovered genes are likely to be rare. We further compared our early-life gene catalog to the large protein databaseUnified Human Gastrointestinal Protein (UHGP)that mainly includes microbial genes from the gut of adults and clustered at 95% protein identity (n=20,239,340)22. This revealed that 2.9 million gene clusters from the ELGP overlapped with the UHGP catalog, but there was a large proportion (27.3%, n=1,076,116) from the ELGP not represented in UHGP, and the total number of proteins from 1,076,116 clusters accounted for 5.4% when taking all 86,678,654 genes into consideration, underlying the uniqueness of the gut microbiome of children. Among those protein cluster representatives exclusively from ELGP or UHGP, 27.6% (n=296,624) and 30.1% (n=3,972,835) of representatives were respectively annotated with a known function, and the rest of the clusters were either putative or hypothetical proteins (Fig.2b). Therefore, our results provide a comprehensive collection of the gut microbiome protein space early in life that may serve as a reference for early-life gut microbiome research.

a Rarefaction analysis of the number of protein clusters of early-life gut microbiome at 95% amino acid identity as a function of the number of genomes included. Curves are depicted for all the protein clusters and after excluding singleton protein clusters (containing only one protein sequence). b Overlap between the ELGP (orange) and UHGP (blue), both clustered at 95% amino acid identity. The bars at bottom indicate the number of proteins that the cluster representatives from three categories (ELGP exclusive, Overlap, and UHGP exclusive) encode, stratified as known, putative, and hypothetical proteins. c Number of proteins with functional annotation across the five functional categories and their degree of overlap. Vertical bars represent the number of proteins unique (color) to each functional category or shared (black) between the specific functional categories. Horizontal bars in the lower panel indicate the total number of proteins with functional annotation in each functional category. d Dynamics of the rate of protein characterization of ELGP along with the age of children. e COG functional annotation of the ELGP catalog clustered at 95% amino acid identity. Only functions with >5000 genes are plotted. f Dynamics of the rate of COG functional annotation of ELGP catalog clustered at 95% amino acid identity in response to the age of children. Vertical bars from left to right represent the age of children at 0, 1, 3, 6, 12, 18, 24, 30, and 36 months. Asterisk (*) indicates the significant difference (two-tailed Wilcoxon test, FDR<0.05) between the rate of COG functional annotation of ELGP catalog at birth and 36 months old children.

To better elucidate the functional diversity of the early-life gut microbiome, we annotated gene functions of the ELGP catalog with currently available databases, including Clusters of Orthologous Genes (COGs), KEGG modules, level-4 Enzyme Commission categories (ECs), Gene Ontologies (GOs), and carbohydrate-active enzymes (CAZy). We found that a total of 70.5% of genes from the ELGP had a match to at least one of the databases of COGs (n=2,844,021 genes across 24 functional categories), ECs (n=722,946 genes matching 2658 enzymes), KEGG (n=533,759 genes from 674 modules), GOs (n=256,861 genes from 10,461 orthologous groups), and CAZy (n=46,392 genes matching 104 families) (Fig.2c). These results showed that a median of 88.7% (IQR=85.991.0%) of genes per genome in the ELGG were annotated, and this rate was lower in genomes from children at 36 months of age (a median of 89.1% at birth vs. 86.5% at 36 months; linear model, p<0.0001) (Fig.2d). Based on the distribution of COGs functions that matched the largest number of ELGP genes, the most abundant genes with a known function present in the ELGP were involved in transcription, replication/recombination/repair, cell wall/membrane/envelope biogenesis, and carbohydrate transport and metabolism (Fig.2e). The most highly represented families of ECs, KEGG, and GOs were DNA helicase (EC: 3.6.4.12), M00178 (ribosome, bacteria) and biological process (GO: 0008150). The predominant glycoside hydrolase family in the ELGP catalog was GH13, targeting the hydrolysis of a wide range of simple and complex glycans including di-, oligo-, and polysaccharides as well as related substrates, such as starch, amylose, and pullulan30 (Supplementary Fig.1b). We again observed that the majority of the investigated COGs categories (11/19) were well-characterized at the first few months, and then gradually decreased as children aged (i.e., Wilcoxon test, FDR<0.05, when compared to the annotated gene per genomes at birth to that from 36 months) (Fig.2f).

To explore the number of culturable species that were included in the ELGG catalog, we clustered 32,277 MAGs together with 187,555 isolate reference genomes from NCBI RefSeq and two human gut culturing studies11,12. The species-level clusters (SGBs for species-level genome bins) were computed by using a multistep distance-based approach with at least 95% average nucleotide identity (ANI) and at least 30% overlap of alignment fraction (AF) (Methods). A total of 23,307 SGBs were generated, and the MAGs from the ELGG catalog were distributed into 2172 SGBs (Fig.3; Supplementary Data3). Among the 2172 SGBs, only 774 SGBs contained isolate reference genomes (denoted as cSGBs for cultured SGBs) containing 86,283 isolate reference genomes and 29,367 MAGs. A large proportion of 99.8% (n=86,132) of 86,238 isolate reference genomes were near-complete (Supplementary Fig.2). The other 1398 SGBs contained exclusively 2910 MAGs in total (denoted uSGBs for uncultured SGBs), indicating that 64.4% of the ELGG SGBs (9% of total MAGs) lack isolate genomes (Fig.3a). When compared to the 4644 representatives of the UHGG using a distance cutoff of 0.05 (95% ANI), 13.4% of ELGG SGBs lacked a match to the UHGG. By counting the number of MAGs within each SGBs, it was observed that cSGBs represented the largest clusters, while uSGBs tended to be the rarest, with 1003 of uSGBs represented by a single genome, which was in line with the previous studies reconstructing MAGs from the environmental and host-associated microbiota15,16,22. Interestingly, cSGBs with >50% MAGs outnumbered uSGBs with 050% MAGs for clusters containing three or more genomes, underscoring the discovery power of large metagenomic cohorts (Fig.3b). The early-life human microbial phylogenetic diversity of the 2171 bacterial SGBs was increased by 38% with the uSGBs, indicating the utility of these genomes to improve the classification of sequences from the early-life microbiome (Fig.3c). The median pairwise distances of genomes within SGBs was 0.020 (IQR=0.0140.029) when including references and MAGs and 0.020 (IQR=0.0130.029) when only considering MAGs.

a Overlap of SGBs containing both MAGs and isolate reference genomes. SGBs containing MAGs and reference genomes are denoted as cultured SGBs (cSGBs), SGBs without reference genomes are denoted as uncultured SGBs (uSGBs), and those exclusively containing reference genomes are denoted as non-early-life SGBs. b The number of cSGBs and uSGBs as a function of the genome number within each SGBs. The uncultured score is calculated as the proportion of MAGs in the total genomes belonging to that SGB. c The phylogenetic tree of early-life gut microbiome built with 2171 bacterial representative genomes of the ELGG catalog. d The number of cultured taxa at different resolutions from 2172 representative genomes. e The number of MAGs in each SGBs, and only the top 40 most represented SGBs were displayed. The clinical factor (i.e., delivery mode, gestational age, and age) related to the MAGs per species are plotted.

We further taxonomically annotated each species representative using the Genome Taxonomy Database Toolkit (GTDB-Tk) based on the GTDB database consisting of >311,000 bacterial and >6000 archaeal genomes that are comprised of isolate genomes, MAGs, and single-amplified genomes. We found that the ELGG catalog covered 14 known phyla (13 for bacteria and 1 for archaea), 18 known classes (17 for bacteria and 1 for archaea), 55 known orders (54 for bacteria and 1 for archaea), and 382 known genera (381 for bacteria and 1 for archaea) (Fig.3d; Supplementary Data3). Additionally, there were still 214 uSGBs including 339 MAGs that were not classified at the species level, indicating the lack of microbial representation in the current GTDB database. The top five uSGB classified genera were Collinsella (71 uSGBs with 143 MAGs), Streptococcus (33 uSGBs with 43 MAGs), Haemophilus D (13 uSGBs with 17 MAGs), Veillonella (13 uSGBs with 14 MAGs), and Bifidobacterium (9 uSGBs with 16 MAGs). Compared to the UHGG collection that is mainly comprised of microbial genomes from adults22, the phylum Firmicutes_A (705 SGBs with 7,765 MAGs in ELGG catalog) took up the largest proportion of SGBs in both children and adult gut microbiomes, followed by Firmicutes (390 SGBs, 7102 MAGs), Actinobacteriota (359 SGBs, 6188 MAGs), Proteobacteria (336 SGBs, 5409 MAGs), and Firmicutes_C (165 SGBs, 2,007 MAGs) (Supplementary Fig.3a). All these top five phyla in children gut microbiome were represented by over 60% of uSGBs (Supplementary Fig.3b). When compared at higher taxonomic resolution, a distinct difference was observed between children and adults gut microbiota. The MAGs assembled from children gut microbiome mainly consisted of the genus Streptococcus (164 SGBs, 2112 MAGs), Collinsella (129 SGBs, 534 MAGs), Veillonella (89 SGBs, 1501 MAGs), Haemophilus D (78 SGBs, 418 MAGs), and Bifidobacterium (58 SGBs, 4,604 MAGs) (Supplementary Fig.3a); while the top genera from the UHGG catalog were Collinsella, Prevotella, Streptococcus, Bacteroides, and Alistipes.

At species level, the most represented SGBs in the ELGG catalog were Escherichia coli, Enterococcus faecalis, Bifidobacterium longum, Staphylococcus epidermidis, and Bifidobacterium breve, which completely differed from the genomes of the UHGG catalog (Fig.3e). We further stratified the MAGs within each species according to delivery mode [vaginal and cesarean section (C-section)], gestational age (full-term and preterm) and the age of children at sampling. The MAGs belonging to species E. faecalis, S. epidermidis, Clostridium spp., Veillonella spp., Klebsiella spp., and Streptococcus vestibularis were mainly reconstructed from children born by C-section and/or preterm children. These species are potentially pathogenic and commonly associated with the hospital environment4,31. The majority of these MAGs were derived from fecal samples collected within the first year of life, highlighting the specificity of the ELGG catalog for the early-life gut microbiome. Notably, some MAGs were not reconstructed from the first few months after birth, but obtained at a later time, such as Anaerostipes hadrus and Ruminococcus_E bromli_B.

Rarefaction analysis of the total number of SGBs as a function of the number of MAGs indicated that the species from the ELGG catalog has not approached saturation, highlighting that more species remain to be discovered in the gut microbiome of children (Supplementary Fig.3c). However, in line with the rarefaction analysis based on genomes from the UHGG catalog22, this unsaturated status was mainly attributed to rare members of the gut microbiota, as there were 1206 SGBs with only one MAG from the ELGG catalog (Supplementary Fig.3d). When only considering SGBs containing at least two conspecific MAGs, the number of species was much closer to saturation (Supplementary Fig.3c). When looking into the geographic prevalence of SGBs in each continent (i.e., Asia, Europe, North America, and Oceania), the most prevalent species worldwide included E. coli, B. longum, and E. faecalis (Supplementary Fig.4). Meanwhile, there were a number of SGBs with various rates of prevalence in each continent. For example, species of Clostridium spp., Klebsiella michiganensis, Citrobacter freundii, and Clostridioides difficile were more prevalent in the samples of North America, which may be attributable to the high proportion (77%) of fecal samples collected from preterm children.

To investigate the reproducibility of SGBs from the ELGG catalog, we clustered the subset of MAGs with >50% genome completeness and <5% contamination and free of chimerism from a common set of 941 metagenomes from Bckhed et al.32 and Vatanen et al.33 that were available in another two previous human gut MAG studies (i.e., Nayfach et al.13 and Pasolli et al.15) (Supplementary Data4). Different assembly and binning approaches were applied in the three studies, i.e., Pasolli et al. assembled and binned with metaSPAdes and MetaBAT2; Nayfach et al. used MegaHIT and a combination of MaxBin2, MetaBAT2, CONCOCT and DAS Tool for assembling, binning and refinement. We observed that the pattern of MAG number produced from each sample was consistent across thethree studies, but a slight increase (Wilcoxon test, p<0.01) in the total number of MAGs was observed with our pipeline (n=5203) compared to Nayfach et al. (n=4284) and Pasolli et al. (n=4728), respectively (Supplementary Fig.5a). By calculating the proportion of shared SGBs on a per-sample basis with one other study (referred to as SGBs similarity, Methods), the median of SGBs similarity of the current study compared to the other two previous studies reached 100% for both Nayfach et al., and Pasolli et al. (Supplementary Fig.5b). In addition, conspecific MAGs reconstructed from the same samples by different studies had a median ANI and AF of 99.9% and 93.9%, respectively (95.0% AF with near-complete MAGs and 85.3% AF with medium-quality MAGs; Supplementary Fig.5c). These results suggest a high reproducibility of popular assembly and binning tools used in large-scale genome reconstructions, in line with previous comparisons22.

Bifidobacterium represents the dominant genus in the gut microbiota of children and is known as the pioneering microbial member that influences microbiota succession and the capability of the host to utilize prebiotic HMOs early in life. A depletion of Bifidobacterium or their genes for the utilization of HMOs has recently been indicated to be involved in host systemic inflammation and immune imbalance34. Based on the GTDB annotation, we greatly expanded the diversity of Bifidobacterium intraspecies diversity by a range of 4 (B. longum) to 12 (Bifidobacterium kashiwanohense) times compared to the reference genomes belonging to the top eight Bifidobacterium SGBs that contained more than 100 MAGs. The largest SGB is B. longum with 296 reference genomes and 1306 added MAGs, followed by B. breve (107 reference genomes; 830 MAGs), Bifidobacterium bifidum (91 reference genomes; 823 MAGs), and Bifidobacterium pseudocatenulatum (77 reference genomes; 446 MAGs) (Fig.4a). The pan-genome of each SGB is defined as the sum of the genes including core and accessory genes of all the genomes within that SGB35. The ELGG increased the size of the pan-genome per species up to a range of 5385 (Bifidobacterium dentium with 2337 exclusively from MAGs) to 10,759 (B. longum with 3522 exclusively from MAGs) that were higher than the reference genomes (Fig.4b). This may indicate the large proportion of bifidobacterial metabolic functions that have not been uncovered based on current culturing approaches. By quantifying the abundance of these genomes in the metagenomic samples, we found that the relative abundance of bifidobacterial species decreased as children aged from birth to 3 years old (Fig.4d). In addition, we found a lower level of strain heterogeneity in samples from early life (first 6 months), which may reflect the relatively simple dietary components (e.g., breastfeeding) in this period.

a The number of genomes stratified by MAGs and reference genomes. b The pan-genome plot represented by the accumulated number of genes as a function of the number of genomes stratified by MAGs and reference genomes. c The rate of functional annotation across databases of COGs, KEGG, GOs, ECs, and CAZy for each species stratified by core and accessory genes. The number in parentheses indicates the number of genes with functional annotation. d Dynamics of the relative abundance and strain heterogeneity of MAGs in response to the age of children. e The number of gene homologs matched to a well-characterized gene cluster responsible for HMOs utilization from each species. Boxes show the interquartile range (IQR), with the vertical line as the median, the whiskers indicating the range of the data (up to 1.5 IQR), and points beyond the whiskers as outliers. f The glycobiome (columns) colored by the number of genes per genome (rows) of each species annotated with the CAZy database. The log10 scaled value (after adding a pseudocount of 1105 to avoid non-finite values resulting from zero gene) is plotted.

Next, we functionally annotated the pan-genomes of each Bifidobacterium species by mapping them against the broad range of databases including COGs, KEGG, GOs, ECs, and CAZy, and found that a proportion of genes between 30.9% (for B. dentium) and 39.2% (for Bifidobacterium adolescentis) lacked a match to any database. When we stratified the genes as core and accessory, the majority of unmatched genes were accessory (only a proportion of 56.064.6% genes matched), and over 92% of core genes were annotated (Fig.4c). According to COG categories, the replication/recombination/repair, carbohydrate transport and metabolism, transcription, and amino acid transport and metabolism were the most prevalent known functions (Supplementary Fig.6a). In addition, a total of 271 KEGG modules were encoded by the eight bifidobacterial species present in the ELGG (Supplementary Fig.6b; Supplementary Data5), with the main functions relating to multiple sugar transport system (M00207), ribosomal structure (M00178), putative ABC transport system (M00258), and raffinose/stachyose/melibiose transport system (M00196), reflecting their high capabilities of carbohydrate metabolism.

As the main microbial degraders of carbohydrates in the gastrointestinal tract early in life, we further profiled the glycobiome of bifidobacterial species based on the CAZy profiles (Fig.4f). A total of 26 glycoside hydrolases (GH), 7 glycosyl transferases (GT), two carbohydrate-binding modules (CBM), and one carbohydrate esterase (CE) were observed across eight bifidobacterial species including reference genomes and MAGs. Notably, GH13 (followed by GT2, GT4, GH3, and GH31) were the most prevalent CAZy families within the bifidobacterial glycobiome, which has been proven to have the capacity to break down a wide range of carbohydrates dominant in the diet30. Compared to reference genomes, MAGs in the ELGG were annotated with higher and/or distinct gene families involved in carbohydrate metabolism. For instance, the MAGs from B. bifidum contain 27 CAZy families, 10 of which were not found in reference isolate genomes. The CAZy families present in MAGs but absent from reference isolate genomes included GH3, GH5, GH9, GH43, GH127, GH38, CE10, GH8, CBM6, and GH94. Considering breastfeeding during infancy, we further explored the functional potential of the MAGs in terms of HMO utilization by investigating the presence of gene cluster described as involved in HMO transport and degradation in B. infantis (ATCC 15967). MAGs from B. longum subspecies clade, B. infantis, carried a high number of HMO homologs, (236 out of 261 MAGs had at least 15 homologs, accounting for 50% of the HMO gene cluster) (Fig.4e), while only two MAGs from B. longum carried gene cluster related to HMO metabolism, indicating the distinct capacity in HMO utilization of bifidobacterial species. When comparing the relative abundance of B. infantis with other B. longum genomes, a higher (Wilcoxon test, p<0.0001) abundance of B. infantis was observed in all continents except for Oceania (Supplementary Fig.6c), indicating the competitive advantage of B. infantis strains in early life that may be conferred by the presence of HMO gene cluster.

To assess how representative the ELGG is as a genomic reference for metagenomes from the human gut in early life, we compared the mapping rate of 353 child fecal samples aged within the first 3 years against the ELGG catalog and another two large-scale reference collections, i.e., CIBIO (n=4930)15 and UHGG (n=4644)22. Using Bowtie2, we obtained a median mapping rate of 82.8% (IQR=72.788.8%) with the ELGG catalog. This level of classification was higher than that obtained with the CIBIO and UHGG catalogs [69.5% (IQR=61.176.4%) and 71.2% (IQR=62.177.8%) respectively; Wilcoxon test, p<0.0001] (Supplementary Fig.7a; Supplementary Data6). Additional evidence to support the specificity of the ELGG for classification of the early-life gut microbiome was the slightly lower mapping rate [a median of 66.7% (IQR=60.273.2%, ELGG) compared to 72.1% (IQR=69.275.0%, CIBIO) and 73.2% (IQR=69.575.5%, UHGG); Wilcoxon test, p<0.0001] when aligning metagenomic sequencing reads from the adult fecal samples (n=510) against each catalog (Supplementary Fig.7b; Supplementary Data6).

Children born by C-section display a significantly distinct gut microbial acquisition and development in the first few years compared to children born vaginally4,6, and several studies have attempted to restore the gut microbiota by probiotic supplements36, vaginal swabbing37, or fecal microbiota transplantation38 due to this disordered microbiome being positively linked with various diseases later in life39. We, therefore, leveraged the ELGG catalog together with the available metadata to address the taxonomic and functional differences associated with C-section at a genome level. A total of 18,836 and 13,412 MAGs were obtained from vaginally (n=3299 samples) and C-section (n=2612 samples) born children, respectively, with 1 to 38 MAGs per sample (meanSD: 5.714.18) for the former and 1 to 27 MAGs per sample for the latter (5.133.37) (Wilcoxon test, p<0.0001). When adjusting by the sequencing depth, the number of MAGs per million paired reads differed (0.320.35 for vaginal and 0.370.24 for C-section; Wilcoxon test, p<0.001) (Supplementary Fig.8a). The majority of MAGs for either delivery mode were annotated as phyla of Firmicutes/_A/_C, Actinobacteriota, Proteobacteria, Bacteroidota, and Verrucomicrobiota (Supplementary Fig.8b). When stratified by childrens age, the prevalence of the genera Bacteroides/Phocaeicola and Parabacteroides belonging to the Bacteroidota phylum present in C-section born children were at lower levels (Wilcoxon test blocked by children age, p<0.05), while the genera Veillonella and Klebsiella were higher (Wilcoxon test blocked by children age, p=0.035 and p=0.056, respectively) than those born vaginally, in particular in the first few months of life (Fig.5a). This observation confirms and expands the previous results obtained with the read-based analysis4,40.

a Prevalence of 16 bacterial genera in children stratified by delivery mode over time, where each genus was colored by its phylum. Only genera with >10% prevalence in children born by any of delivery modes are plotted. b The explained variance (R2) contributed by delivery mode of 46 species that were significantly (PERMANOVA, FDR<0.05) associated with delivery mode based on the hamming distance of core genes per species. The number in parentheses indicate the number of MAGs of this species. c The number of genes that were prevalent in C-section born children or vaginally born children (>70% in C-section born children and <30% in vaginally born children, and vice versa) for each species and their associated functions annotated by COGs database. d The density of antibiotic resistance genes (ARGs) richness in each genome of ELGG, and the taxonomic assignment of the genomes at genus (left inset) and species (right inset) level. e The dynamics of ARGs richness from the early-life human gut microbiome in response to the age of children. The gut microbiome from children born byC-section carried higher (two-tailed Wilcoxon test, p<0.05, inset) number of ARGs than that of children born vaginally. The inset boxes show the interquartile range (IQR), with the horizonal line as the median, the whiskers indicating the range of the data (up to 1.5 IQR), and points beyond the whiskers as outliers.

Beyond the observed differential taxa, the reconstructed genomes enabled us to explore the intraspecies genetic and genomic diversity of the gut microbiome associated with delivery mode in early life. Only SGBs with at least 10 conspecific near-complete genomes (>90% completeness and <5% contamination) from both vaginal and C-section born children were considered in this part of the analysis. A total of 116 species were retained, covering the phyla Firmicutes/_A/_C (n=30/34/7), Proteobacteria (n=20), Actinobacteriota (n=16), Bacteroidota (n=7), and Verrucomicrobiota (n=2), totaling 20,816 genomes (82% of all near-complete genomes of ELGG) (Supplementary Data7). When looking into the intraspecies genomic diversity, the average pairwise genetic distances of core genes for each SGB was below 5% (typically used as a threshold to define bacterial species) (Supplementary Data7). When setting the threshold of ANI at a higher level based on whole genomes, a number of subspecies from 1 to 88 and a range of 2 to 596 were obtained at a cutoff of 97% and 99%, respectively, suggesting the existence of diverse subspecies populations (Supplementary Data7). We further sought to determine to what extent delivery mode contributed to these variances. The intraspecies variation within the core genomes of 46 species, and the genomic distances (based on gene presence/absence) of 64 species were significantly (PERMANOVA, FDR<0.05) influenced by delivery mode with effect size up to 18.4% and 17.3%, respectively (Fig.5b; Supplementary Fig.8c). Notably, Streptococcus agalactiae also known as group B streptococci was highly sensitive to genetically associate with delivery mode.

The pan-genome size of the 116 species here analyzed ranged from 1788 (Negativicoccus succinicivorans, n=58 genomes) to 25,698 (Phocaeicola dorei, n=677 genomes) (Supplementary Data7). A total of 31,976 unique genes across 116 species were observed with varying levels of prevalence among genomes from children born vaginally or via C-section. Functions encoded by the genes prevalent (>70%) in C-section born children but not children born vaginally (<30%) were mainly involved in carbohydrate transport/metabolism, cell motility, transcription, and cell wall/membrane/envelope biogenesis (Fig.5c). The majority of differentially prevalent genes were not related to HMO degradation and utilization as only 4738 unique genes (out of 31,976) were matched with a HMO gene cluster from strain B.infantis ATCC 15697.

Mothers who give birth by C-section usually undergo antibiotic treatment, which may result in different antibiotic resistance profiles reflected in the gut microbiome of children. We thus functionally annotated the genomes with antibiotic resistance genes (ARGs) based on the Comprehensive Antibiotic Resistance Database (CARD). The average ARG richness per genome from C-section born children was higher (Wilcoxon test, p<0.0001) than that of vaginally born children (11.6 vs. 10.0 type of ARGs), however, both distributions of ARG richness of genomes from either delivery mode were clearly trimodal (Fig.5d), with a larger peak at only one ARG, and the other two smaller peaks at 31 and 50 genes, respectively. The origins of ARGs within each peak differed among children born by different delivery modes. In the second peak, genera Klebsiella, Enterobacter, and Citrobacter were the main contributors in children born by C-section; while the third peak was mainly contributed by E. coli that was more prevalent in vaginally born children. Apart from E. coli, 73 MAGs from Pseudomonas aeruginosa were found to carry higher (Wilcoxon test, p<0.0001) richness of ARGs (58.81.38) than E. coli (50.22.27). Among these 73 MAGs, 68 were reconstructed from preterm children within the first 6 months (62 genomes within the first month). As children aged, the richness of ARGs in the gut microbiome generally decreased, from an average of 42.6 at one month to 6.8 ARGs at over 36 months old (Fig.5e). Notably, the richness of ARGs present in the gut microbiomes of children born by C-section was overall higher than that of vaginally born children (an average of 36.9 vs. 32.5 AGRs; Wilcoxon test, p<0.0001). When comparing the genomes within the same species from children born differently in terms of ARG richness, 15 species showed differential ARG richness, and 12 species contained higher numbers (Wilcoxon test, p<0.05) of ARGs in C-section born children than those born vaginally, while three species (Pauljensenia radingae_A, Clostridium paraputrificum, and Clostridium_P perfringens) exhibited opposite patterns (Supplementary Fig.8d). The most common mechanisms of antibiotic resistance discovered in the 20,816 genomes included antibiotic efflux, antibiotic target alteration, and antibiotic inactivation (Supplementary Fig.8e).

The comprehensive catalog of the early-life microbiome enabled us to explore the taxonomic and functional differences between the children and adult gut microbiomes at a genome level. We thus compared the five most represented genera in children (3 years) and adults (18 years) (i.e., Alistipes, Bacteroides, Bifidobacterium, Prevotella, Streptococcus, Veillonella) based on ELGG and UHGG catalogs22, totaling 12 species with >60 near-complete genomes (>90% completeness and <5% contamination). The pan-genome size was positively associated with the number of included genomes, but none of the species reached a plateau, even Bacteroides uniformis with the highest number of genomes (n=1087) containing 32,215 genes in adults. Species of Streptococcus thermophilus had the lowest pan-genome size with 2572 for adults and 2639 for children from 143 and 136 genomes, respectively (Fig.6a). This suggests additional genomes from each species remain to be discovered across populations. In the genus Bacteroides, genomes from adults contained a higher number of unique genes than those from children when considering the same number of genomes (Wilcoxon test, FDR<0.05). In contrast, gene numbers of Alistipes onderdonkii, B. adolescentis, B. longum, B. pseudocatenulatum, and Streptococcus salivarius were higher (Wilcoxon test, FDR<0.05) in children (Fig.6b; Supplementary Fig.9).

a Number of genomes (bar plot) and pan-genome size of each species from children and adults. b Pan-genome plot represented by the accumulated number of genes against the number of genomes of B. ovatus and B. pesudocatenulatum stratified by children and adults (two-tailed Wilcoxon test, *FDR<0.05). c The explained variance (R2) contributed by age (children and adults) based on the hamming distance of core genes per species and Jaccard distance of presence/absence genes (two-tailed Wilcoxon test, *FDR<0.05). d The unique functional annotations belonging to either children or adults categorized by databases of COGs, KEGG, GOs, ECs, and CAZy.

Notably, when looking into gene diversity (estimated by the Jaccard distance based on the presence/absence of genes per genome), genomes from adults showed higher (Wilcoxon test, FDR<0.05) gene diversity on average than that of children for 5 out of 12 species, including Bacteroides fragilis, Bacteroides ovatus, B. bifidum, S. salivarius, and S. thermophilus (Supplementary Fig.10a). These results indicate that genomes within these species in early life are more conserved, and more specific genes are acquired by the microorganisms later in life. On the contrary, the enriched species B. longum showed higher (FDR<0.05) gene diversity than that of adults. We also explored the effect size and significance of age (3 years for children and 18 years for adults) on the gene diversity of each species. The results showed the distinct contribution of age (PERMANOVA, FDR<0.05) to the genetic variation of species between children and adults. S. salivarius (R2=0.047 for hamming distance and R2=0.037 for Jaccard distance), B. pseudocatenulatum (R2=0.032; R2=0.039), and S. thermophilus (R2=0.027; R2=0.040) were the species most significantly associated with age (Fig.6c).

Based on the multiple functional annotation schemes as ELGP, the pan-genome of species showed comparable rates of gene annotations between children and adults, but differed across species, namely, B. adolescentis with the lowest rates of 62.4% and 64.6% for children and adults respectively, and S. thermophilus with the highest respective rates of 84.6% and 85.3% for children and adults (Supplementary Fig.10b). Based on the CAZy annotation of the pan-genomes, we found that gut microorganisms from children harbored a higher (Wilcoxon test, FDR<0.05) number of specific CAZy families, most notably GH13, GT4, GT2, GH43, and GH3 (Supplementary Fig.10c). Additionally, we sought to determine the functions that were unique to either children or adults. We found a large number of EC families among species (n=138 in children and 057 in adults), KEGG modules (n=023 in children and 013 in adults), CAZy families (n=04 in children and 07 in adults), COGs (n=01 for both children and adults) and GOs categories (n=861 in children and 057 adults) to be specific to either children or adults (Fig.6d; Supplementary Data8). Within EC families of B. bifidum, the enzymes of GlfT2 (EC 2.4.1.288; n=27 genomes), asparagine synthase (EC 6.3.5.4; 17 genomes), and D-xylulose reductase (EC 1.1.1.9; 12 genomes) were the most prevalent in children; and the top families of CAZy from children included GH127 (5 genomes), GH94 (4 genomes), and CBM6 (1 genomes). Within EC families of B. uniformis, the enzymes of dTDP-6-deoxy-L-talose 4-dehydrogenase (EC 1.1.1.34; 13 genomes), thiamine kinase (EC 2.7.1.89; 13 genomes), and histidine decarboxylase (EC 4.1.1.22; 13 genomes) were the most prevalent in adults; and the top families of CAZy from adults included PL4 (7 genomes), PL11 (3 genomes), AA10 (2 genomes), and CBM73 (2 genomes).

Continued here:
A compendium of 32,277 metagenome-assembled genomes and over 80 million genes from the early-life human gut microbiome - Nature.com

Researchers at the Chinese Academy of Sciences report fully and successfully recombining the chromosomes of a living mouse, an animal named Xiao Zhu, or Little Bamboo.

In a laboratory at the Chinese Academy of Sciences, one unassuming mouse called Little Bamboo is, in fact, the first of its species a man-made species. This mouses genome has 19 pairs of chromosomes, one fewer than natural, and its all due to the meddling of human scientists.

The team in Beijing fully recombined the mouses genes through a process whereby its chromosomes were broken down into various segments and then put back together in a new set-up. This is the first time such a process was carried out on the scale of a living organism without severely impacting its ability to survive. This means that Little Bamboo is, in effect, the first individual of a completely new and man-made species of rat, and the worlds first mammal with fully recombined genes.

Mammalian genomes are much more complex than yeast genomes, and complete chromosomal rearrangements in mammals have remained unsuccessful, said lead author Li Wei, a researcher with the Institute of Zoology at the Chinese Academy of Sciences in Beijing, for the state-owned Science and Technology Daily.

Chromosomes are condensed DNA strands bunched together in different shapes which help keep DNA tidy in a cells nucleus. They are roughly equivalent to a compressed digital document, if you will, helping the data occupy less space on the hard drive of cells.

These bunches of DNA naturally break down and recombine during sexual reproduction, when pieces of each parents chromosomes bind to the other parents equivalent chromosome pieces to form an entirely new genome that inherits parts of both. This process is very complicated and delicate, and errors here can cause quite a lot of issues for any affected offspring. Researchers have been trying to interfere with this process to help address such errors when they happen, but weve had extremely limited success and what success we did have was only using single-cell organisms like yeast.

But the current study showcases that such interventions can be performed, even in living organisms, paving the way for synthetic biology to tackle a whole range of new experiments.

For the paper, Li and his colleagues used the gene-editing tool CRISPR. This is based on natural gene-modification processes and acts much like a scissor-and-glue, allowing researchers to cut DNA strands in specific areas and weld in new bits, before tying the string back together. They used CRISPR to manipulate the chromosomes contained by a unique reproductive stem cell the mouse which they created specifically for this experiment.

Previous attempts by the team resulted in those recombination errors we mentioned earlier. The issues arose when they tried to stitch together two very long chromosome pieces, which would attach imperfectly. These cells would go on to develop into unhealthy, deformed specimens or ones that exhibited strange behaviors, or would make the animals unviable outright, causing them to die.

Their answer was to use shorter chromosome sequences and reduce the total number of chromosomes to 19 pairs, one fewer than mice have naturally. Through this approach, they managed to create a new species which, despite having a completely different chromosome package in their cells compared to natural mice, appears to be completely healthy and show normal behavior.

The recombined mice were then allowed to mate with un-modified animals, which did result in successful pregnancies, albeit at a relatively low rate. The offspring of these pairs contained the manipulated chromosomes of their parents, showcasing that the effects of such gene editing can extend through the generations.

This means that, for the first time in the world, we have achieved complete chromosomal rearrangement in mammals, making a new breakthrough in synthetic biology, said Li, according to the South China Morning Post. This research is a breakthrough in bioengineering technology, helping to understand the impact of large-scale remodelling of mammalian chromosomes, and to gain a deeper understanding of the molecular mechanisms behind growth and development, reproductive evolution, and even the creation of a species.

Due to the observed ability to conserve genes across generations, the team is confident that their approach could help researchers study how genetics influence conditions like infertility or cancers, and how they could be treated.

Although the experiment had been approved by the Chinese Academy of Sciences research ethics committee, the use of CRISPR on human embryos is currently strictly prohibited in China, so the team has taken many steps in this direction.

Original post:
Chinese researchers create the first successful, living mammals with a fully-reconfigured genome - ZME Science

This week the National Human Genome Research Institute is holding a meeting to discuss progress and identify solutions to genomic medicine implementation challenges. Participants from academic medical centers are presenting examples of how genomic learning healthcare systems apply cycles of genomic medicine implementation, evaluation, adjustment, and updated implementation practices across their delivery systems.

One of the speakers was Travis Osterman, D.O., M.S., a medical oncologist, informatician, clinical director in the Office of the Chief Health Information Officer at Vanderbilt University Medical Center (VUMC), and director of Cancer Clinical Informatics in the Vanderbilt-Ingram Cancer Center. His talk focused on progress made integrating clinical genomic results into the electronic health record and some of the downstream benefits Vanderbilt is seeing from that.

Osterman gave the audience an update on some recommendations in a 2015 National Academy of Medicine report on this topic. The first was establishing data standards and common ways of representing outcomes that would facilitate the scalability and translation of genomic information into clinical care. The second was integrating genomic data into the clinic through clinical decision support, so the guidance is scalable and interoperable.

He praised the HL7 clinical genomics working group for its work on an evolving data standard for transmitting clinical genomics information that includes pharmacogenomics, somatic and germline information. The standard for trial use was initially published in November of 2018. Today Vanderbilt is one of 29 healthcare systems that are utilizing this data transmission standard to connect to reference laboratories and receive structured information, Osterman said.

Our tagline at Vanderbilt is making healthcare personal and re-examining our efforts in precision medicine led to a clinical genomics workstream, which I help to lead, Osterman said. Many of these efforts were already ongoing, and it was bringing them together so we could leverage scale, understand what each of what we jokingly call silos of excellence were doing and make that work across our enterprise.

In terms of clinical decision support, Osterman said, most health systems are either recommending genomic testing or personalizing care for an individual patient. That is what we did from 2010 through 2020, and we have a robust pharmacogenomics program. We started that program in 2010, and currently have almost 20 drug-gene interactions that we track. We're both recommending that test in our electronic health record, and then able to integrate those results to help make sure that the right patients are getting the right drugs the right time, he explained. This is not necessarily a new phenomenon, but we're trying to expand those same principles into other spaces.

Osterman spoke about working with Kevin Ess, M.D., Ph.D., who is a pediatric neurologist at Vanderbilt. Ess shared that about 40 percent of pediatric seizure disorders are due to genetic factors. The ones that aren't potentially have an anatomic cause and therefore can go to surgery and potentially have a really good outcome.

Informaticists worked with Ess to identify which specific patients may actually need testing to identify a genetic cause of their seizure disorder, and they developed a clinical work flow to make it happen.

This is live in our system, and is one of the ways that we're trying to push forward into spaces that are outside of pharmacogenomics, Osterman said. We've had several other examples. Routinely, for the last many years, we've been doing both recommended genomic testing and personalized care for individual patients, but I think we need to do more. We need to think not just about individual patients; we need to think about how we do this is for patient populations. We need to think about how we recommend genetic testing or genomic testing to patient populations. Then how do we care for and deliver care to the entire population to personalize their care.

To do this, Osterman said, we need to get the data out of PDFs. We want to receive all of those discrete final results, just like any other internal lab that we would order. We want to flow downstream into the electronic health record, and then ultimately both provide patient care and then support our research enterprise, he added. We don't want to give up all of those raw unprocessed files, whether that's from our internal genomics lab or whether that's from an external partner. Those are going to go into operational data storage and still may help provide patient care, because we have an internal lab that may leverage those upstream files. Those are going to go into a research repository.

Vanderbilt went live on Epic's genomics module, which allows health systems to receive those structured results from third-party reference laboratories, in July of 2021. The real value isn't me receiving those genetic results in my in-basket in our electronic health record, Osterman said. The real value is how this benefits all of our downstream processes. The order goes out and the variant data come back into Epics database, which is called Chronicles, and it's immediately available to patients. We've talked about putting patients at the center of this. If I have a test that I ordered on one of my patients, that patient can then take that test and show it to another provider, regardless of what electronic health record they're using, because they'll have those data on their phone, he said.

We're now able to leverage some of the tools within our electronic health record to do queries that we would otherwise need to do with one of our business intelligence tools. For instance, our electronic health record has the ability to do some kind of rudimentary data visualizations through a tool called Slicer Dicer. And because these are structured data, those work right out of the box, and all of my colleagues can access those data as well, he said. Because these are structured data flowing through all of our database systems, they also flow downstream to our research system, called the Research Derivative, which is a copy of our electronic health record use for research. And then that is de-identified in something called the Synthetic Derivative, which then is linked up to our biobank Bioview. By putting these data in structured form, not only are we improving patient care, but these data flow all the way downstream.

Here is another example of process improvement: Before we implemented the structured data, when a new first-class drug became approved for cancer treatments, we had data scientists that would query multiple vendor systems; they would take that query, and they would look against our electronic health record system to see which of those patients were still living and who their oncologists were, Osterman explained. Then they would give us a report on which patients would potentially benefit from these new treatments after their FDA approval. This process took about one to two weeks, but we thought it was high value, and so we absolutely supported that. I treat primarily lung cancers, and we have 22 approved drugs with targeted variants, and that number continues to grow every year. Now, since we moved to receiving structured data, this process is much, much shorter. When we know that there's a new first-class drug approved, the clinical team can do these queries directly within Epic.

So how does Osterman think the progress on a genomics learning health system is going overall? Well, as far as establishing data standards, through the work of HL7, I think we've made tremendous progress, he said. And second, integrating clinical decision support, I think we're pushing the envelope. I think there are opportunities to do this even at the population level today.

What are the next challenges? One is providing a standard way for patients to provide and transfer genetic and genomic information, he said. I think that's certainly going to be key going forward. I'll take it one step further and say that patients are going to be consenting for large consortium studies and healthcare systems are going to then be asked to share those data with that research consortium, and we need to figure out ways to do that. Finally, I think education is going to be key for both physicians and nurses. This has been a huge effort in our organization.

Original post:
Vanderbilt Sees Downstream Benefits From Integrating Genomic Results Into EHR - Healthcare Innovation

Genomics, the study of the entirety of an organisms genes called the genome, a much newer field compared to the well-known study of genetics, has progressed in the last couple of decades owing to technical advancements in DNA sequencing and computational biology. Genomics as a field is not only advancing healthcare but is also driving sustainable innovation across a whole variety of sectors, be it computing, agriculture, forensics, climate change, and more. Introducing IDCs new whitepaper commissioned by Lenovo & Intel, called Leveraging High-Performance Compute Infrastructure to Address the Genomic Data Challenge in Life Sciences,Sinisa Nikolic, Director - HPC & AI, Lenovo ISG AP, engaged in an informative discussion with Dr. Harsh Sheth, Assistant Professor and Head of Advanced Genomic Technologies Division, FRIGEs Institute of Human Genetics.The revolution of the genomics industry began in the 19th century, way back when genome sequencing required scientists to hold up an x-ray photograph and manually read 400-500 DNA letters a day from a base of 3.3 billion bases in the human genome. It is no surprise that it took 13 years to sequence one complete human genome, and at the cost of approximately $3 Billion. Speaking about how genome sequencing has evolved over the last few decades, Dr. Sheth explains that today genome sequencing of an individual is possible in 2-3 days and that in a span of 40 years, the cost of genome sequencing has reduced from $3 Billion to less than $1000. Elaborating further on the advancement of genome sequencing, Dr. Sheth said, For the last three of four decades, it has been a dream of a scientist or a doctor to provide results in a short time frame to the patients. The advancements in the last four decades have been so huge that genetic test results can be provided in a few days. Opining on the technological barriers impacting the genomics revolution, Sinisa said, Organizations have limited time and limited resources to develop some of these genomics technologies [...] what they want is a blend of pre-packaged technologies, and Lenovo was and is best positioned to work with these organizations given its long and storied history in HPC and very strong focus on genomics.

Elaborating on the challenges faced by genomics researchers, Sinisa recalls findings from the IDC report regarding infrastructure challenges across the industry. He explains that 28 per cent of Asian respondents said their existing infrastructure is not scalable enough, 20 per cent said their current solution is complex, and 20 per cent said too much customization is required. With a consistent focus to address some of these industry-wide challenges, Sinisa explained how Lenovo collaborated with Intel using Genome Analysis Toolkit (GATK) open-source code an HPC architecture poised to revolutionize genome sequencing. We optimized and tuned that for our hardware infrastructure, front of mind was to use off-the-shelf components, keeping the costs for our clients to minimum [] we call it GOAST, he adds. Sinisa further explains how with the Genomics Optimization and Scalability Tool (GOAST), Lenovo had reduced the processing time of a whole human genome from 60 - 150 hours to 24 48 minutes. He further elaborated how GOAST can increase lab productivity, improve time to data and potentially save lives through all the discoveries.

Lenovo ISG partnered with Delhi Universitys Center of Genetic Manipulation of Crop Plants (CGMCP), looking to improve and breed more nutritious, drought and disease-tolerant, high-yield plants to feed the world. Lenovo deployed its GOAST solution at CGMCP to accelerate time to insights - 48 hours to just 6 hours.

Dr. Sheth adds, The COVID-19 pandemic is a wonderful example of where genomics came to the rescue. Never in the history of mankind has a vaccine been developed within a year. He further explains how technological advancements in genomics helped create the genetic architecture of the COVID-19 virus even before it was declared a pandemic, which only helped rapidly accelerate the development of vaccines and begin Phase 1 trials early into the pandemic, which was spreading at a massive pace. He adds that genomics is also being used to address multi-drug resistance in various diseases, and oncology has changed how cancer treatment is delivered through personalized treatment. Lenovo has further collaborated with the CSIR Institute of Genomics, and Integrative Biology (CSIR-IGIB), New Delhi, in a unique partnership that uses GOAST to advance cancer research by digging deeper into the genetic roots of the disease.

In conclusion, Sinisa draws light on precision medicine as The Next Big Thing that will drive the genomics revolution on the back of technological advancements. He elaborated how genome sequencing and genome analytics tools (such as GOAST) are helping the world understand biology and genetics better and would allow faster and more accurate care for patients. In explaining how GOAST technology will evolve, he states that Lenovo is working closely with software development teams to build many technology efficiencies which will ultimately impact humanity positively.

(Brand Connect Initiative)

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Lenovo ISGs GOAST Solution Powering genome sequencing with smarter IT - ETHealthWorld

As an associate professor in Genomic Medicine, Linghua Wang, M.D., Ph.D., studies how normal cells become cancerous and how cancer cells develop resistance to drugs.

She and her lab are working to identify the earliest events during tumor progression from precancerous diseases to discover new biomarkers and targets for the development of effective interception strategies.

The lab is also focused on understanding cellular plasticity, which is how cancer cells adapt to the microenvironment and avoid being attacked by the immune system or cancer treatments.

Cellular plasticity contributes to cancer development, progression and metastasis. If we can better understand this process, we can develop effective treatment strategies to overcome drug resistance, Wang says.

As she looks toward the future, she reflects on the challenges shes overcome to get to where she is today.

A desire for a different path

Growing up in a small village in China, Wang was expected to follow a traditional path for young women to become a housewife and take care of children. In fact, with three younger brothers at home to take care of, she wasnt supposed to advance beyond middle school.

I knew I wanted a different life for myself, Wang says. She worked hard and earned great grades, which caught the attention of her teachers and the school principal, who encouraged Wangs parents to let her continue her education.

Wang did so well in school that she earned scholarships to pay for college, where her love of learning grew. I never had books of my own to read growing up, she says. The first time I saw the library, I couldnt believe there were so many books.

Earning an M.D., then Ph.D.

Wang grew up with the goal of becoming a doctor so she could help people. After medical school, she earned a license to practice ophthalmology. But a few months later, her husband was admitted to a Ph.D. program in Tokyo, Japan.

I didnt want to live apart, but I knew I would have had to start my medical school all over again to be able to practice in Japan, so I decided to move with my husband and find a new career, she says.

For the first few months in Japan, Wang wasnt sure what she wanted to do with her life. But she did know one thing: I didnt want to be just a housewife, so I started looking for a job that would keep me constantly learning. She was hired as a research fellow in a cancer genetics laboratory.

I learned about cancer cells and couldnt wait to learn more, she says. So, she enrolled in a Ph.D. program in cancer genomics at the University of Tokyo, studying pancreatic cancer.

It opened a whole new world for me and fueled my passion, Wang says. I realized that studying the cancer genome can transform cancerdiagnosis and treatment and help cancer patients. After that, I was hooked on cancer genomics and data science.

Making connections at MD Anderson

After earning her Ph.D., Wang and her family moved to Houston in 2012, where she completed her postdoctoral training at Baylor College of Medicine and joined their research faculty.

She wanted to become an independent investigator so that she could build and grow her own lab. In 2016, she was invited to speak at the Annual Human Genome Meeting, where she met Andy Futreal, Ph.D., chair of Genomic Medicine at MD Anderson.

I walked up to him and asked if he had any tenure-track faculty positions, she recalls. I felt so lucky to meet Dr. Futreal, who recruited me to MD Anderson. He is always there whenever I need his support and he provided the platform for me to find my own way to shine.

Wang credits MD Andersons team science approach for her interest in establishing a lab here in 2017. MD Anderson is an exceptional place to work with resources and facilities unlike anywhere else. We have so many talented scientists here, and it is such a wonderful place to collaborate. Working closely as a team, were making meaningful contributions to patient care, she says.

Wangs lab aims to harness the potential of big data to fight cancer. Im thrilled about the future of big data in cancer care and the work were doing in the lab. I want to bring in new researchers who love the work and are just as motivated and ambitious as I am.

Finding a balance between work and home life

With three young kids at home, Wang says being a mother helps her be a better leader. Parenthood has taught me to communicate more effectively, and to be more compassionate with members of my lab, she says.

It also helps her manage her time. I have a very busy schedule, constantly going from one meeting to the next, and with tight deadlines for grants and manuscripts, she says. So, I have to manage my time wisely to make sure I can spend quality time with my family, too.

Outside the lab, she likes to travel with her family and finds that cooking meals for them feeds her creative side. I love testing new recipes and seeing my family enjoy trying something new, she says. Cooking is my mental break, and its nice to make something without having to look at a screen, like I do throughout the workday.

The future of genomic medicine

Wang believes the rise in data science, machine learning and artificial intelligence will advance precision and predictive oncology and accelerate drug development.

We will be able to accurately predict patients response to therapy as well as the risk of recurrence and adverse effects and choose the best possible treatment for patients, she says.

And, perhaps most importantly, by using big data and predictive analytics to determine cancer risk, Wang believes researchers will be able to identify better biomarkers to detect cancer early and develop better prevention strategies to reduce the risk of getting cancer.

I expect to see successful integration of data science and clinical practice in the near future, Wang says.

Request an appointment at MD Anderson online or by calling 1-877-632-6789.

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From a small village in China to MD Anderson: Genomic medicine researcher looks to the future of big data in cancer care - MD Anderson Cancer Center

And thats where the now-extinct thylacine comes in.

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Last month, US biotech Colossal Biosciences announced it would invest $10 million in a University of Melbourne team working to bring the Tasmanian tiger back from extinction.

The funding will allow a team of about 50 scientists across Melbourne and Texas to work on the project, initially for three years.

Some of the same techniques used to map the full genome for the smoky mouse will be used on the thylacine project, including growing living cells of threatened species, storing them in a cryobank, and using the DNA from those cells for ongoing research.

The Melbourne Museum has a liquid nitrogen facility called the Ian Potter Australian Wildlife Biobank the animal equivalent of a seed bank that houses the museums existing collection of more than 44,000 tissue, feather and fur samples at minus 185 degrees.

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Cryobanking, or cryopreservation, is the process of cooling and storing cells, tissues, or organs at very low or freezing temperatures to save them for future use.

Previously, the smoky mouse DNA was extracted from skin samples from the ear. But now the institute is growing living smoky mouse cells, so there will always be a store of living DNA from threatened species.

We can preserve living cells as an indefinite resource to maintain living genomic variation, should we require it as part of animal husbandry, said Dr Kevin Rowe from the Museum Victoria Research Institute.

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De-extinction and the recovery of nature lost will only succeed if we as a species are inspired to make the investments needed in nature research and in healing nature.

The smoky mouse genome, the thylacine project and the ethical debate around resurrecting extinct animals will be discussed at the museums next Future Forum on October 6.

Get to the heart of whats happening with climate change and the environment. Our fortnightly Environment newsletter brings you the news, the issues and the solutions. Sign up here.

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This little furball is helping to map a course toward the return of the thylacine - Sydney Morning Herald

An international course strengthened the capacities of laboratories in the region to monitor genetic changes in viruses.

Panama, Aug. 26, 2022 (PAHO)- Representatives from 17 public health laboratories in the region came together this week for the 26th edition of the Viral Evolution and Molecular Epidemiology (VEME) course in Panama. The training, which was organized by the Instituto Conmemorativo Gorgas de Estudios de la Salud (ICGES) in Panama, the Oswaldo Cruz Foundation (FIOCRUZ) in Brazil, and the Pan American Health Organization (PAHO), aims to strengthen genomic surveillance in the Americas.

"Studying the evolution of viruses is key to detecting mutations or variants that can modify the transmission rate or severity of a pathogen and affect the efficacy of diagnostic tests, vaccines and treatments," said Jairo Mndez, emerging viral disease advisor at PAHO. "This is something we experienced with SARS-CoV-2, so we must deepen genomic surveillance for any emerging or re-emerging viruses," he added.

More than 120 people from around the world participated in the 26th edition of VEME, a course that originated at the University of Leuven, Belgium, more than 25 years ago. Around 50 experts in bioinformatics from renowned scientific institutions from 15 countries delivered the training that took place from August 21 to 26 in Panama. Participants from the region were supported through PAHO with funds from the United States Government.

The course consisted of theoretical and practical sessions divided into four modules, ranging from the generation of data from genomic sequencing to more complex analysis of these sequences. For the first time, VEME also included a module aimed at managers and decision-makers.

Dr. Carlos Senz, Secretary General of the Nicaraguan Ministry of Health, considered the training to be "extremely important" both for the technicians who carry out genomic sequencing and for decision-makers like himself. "The course has provided tools to link the epigenetic situation, genomic sequencing and molecular epidemiology information to political and strategic decision-making at the level of each country," he said, highlighting the relevance of "integrating technical approaches with transdisciplinary participation for the resolution of complex problems."

Genetic sequencing and analysis provide insights into the evolution of a virus and its variants, as well as its geographic- and temporal dispersion. The timely analysis of the data serves to identify signs or changes that can have an impact on the behavior of the virus and on health tools and measures. In addition, the information obtained can be complementary to guide the response to an epidemic or pandemic.

"This type of bioinformatic analysis is not something that is commonly done in public health laboratories in the region because it requires training and education," said Alexander Martinez Caballero, Director of the Department of Genomics and Proteomics Research at the Gorgas Institute in Panama. "From now on, many laboratories will be able to perform these analyses in their facilities in a timely manner and for various viruses of interest, such as monkeypox and others that may appear," he said.

Since the beginning of the COVID-19 pandemic, the sequencing capacity to monitor SARS-CoV-2 and its variants has been expanded in the region with the support of PAHO and the Regional COVID-19 Genomic Surveillance Network (COVIGEN), which includes laboratories from more than 20 countries in the Americas.

PAHO has provided training to strengthen genomic sequencing and to integrate it into epidemiological surveillance in the countries. Since 2020, COVIGEN has performed more than 426,000 sequences of SARS-CoV-2 in Latin America and the Caribbean.

The VEME course is one more action to strengthen surveillance and is aligned with the Regional Genomic Surveillance Strategy for Epidemic and Pandemic Preparedness and Response, which will be discussed in September by health leaders of the Americas during PAHO's 30th Pan American Sanitary Conference in Washington.

Read more from the original source:
PAHO strengthens genomic surveillance in the Americas - Pan American Health Organization

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Read more:
China Genome-Based Drug Industry Forecasts 2022: Demand to Grow by 9% Through 2031 - ResearchAndMarkets.com - Tullahoma News and Guardian