Diversity time-period and diversity-time-area relationships exemplified by the human microbiome

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

Transcriptome analysis of embryonic muscle development in Chengkou Mountain Chicken

BACKGROUND

Muscle is the predominant portion of any meat product, and growth performance and product quality are the core of modern breeding. The embryonic period is highly critical for muscle development, the number, shape and structure of muscle fibers are determined at the embryonic stage. Herein, we performed transcriptome analysis to reveal the law of muscle development in the embryonic stage of Chengkou Mountain Chicken at embryonic days (E) 12, 16, 19, 21.

RESULTS

Diameter and area of muscle fibers exhibited significant difference at different embryonic times(P < 0.01). A total of 16,330 mRNAs transcripts were detected, including 109 novel mRNAs transcripts. By comparing different embryonic muscle development time points, 2,262 in E12vsE16, 5,058 in E12vsE19, 6139 in E12vsE21, 1,282 in E16vsE19, 2,920 in E16vsE21, and 646 in E19vsE21differentially expressed mRNAs were identified. It is worth noting that 7,572 mRNAs were differentially expressed. The time-series expression profile of differentially expressed genes (DEGs) showed that the rising and falling expression trends were significantly enriched. The significant enrichment trends included 3,150 DEGs. GO enrichment analysis provided three significantly enriched categories of significantly enriched differential genes, including 65 cellular components, 88 molecular functions, and 453 biological processes. Through KEGG analysis, we explored the biological metabolic pathways involved in differentially expressed genes. A total of 177 KEGG pathways were enriched, including 19 significant pathways, such as extracellular matrix-receptor interactions. Similarly, numerous pathways related to muscle development were found, including the Wnt signaling pathway (P < 0.05), MAPK signalingpathway, TGF-beta signaling pathway, PI3K-Akt signaling pathway and mTOR signaling pathway. Among the differentially expressed genes, we selected those involved in developing 4-time points; notably, up-regulated genes included MYH1F, SLC25A12, and HADHB, whereas the down-regulated genes included STMN1, VASH2, and TUBAL3.

CONCLUSIONS

Our study explored the embryonic muscle development of the Chengkou Mountain Chicken. A large number of DEGs related to muscle development have been identified ,and validation of key genes for embryonic development and preliminary explanation of their role in muscle development. Overall, this study broadened our current understanding of the phenotypic mechanism for myofiber formation and provides valuable information for improving chicken quality.

Predicting the Outbreak Risks and Inflection Points of COVID-19 Pandemic with Classic Ecological Theories

Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID-19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID-19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor's power-law) and Ma's population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell's neutral theory, and Ma's diversity-time relationship adapted for the infection-time relationship. Fisher-Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b-values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID-19. (ii) The infection aggregation critical threshold (M 0) adapted from PACD varies with time (outbreak-stage), space (region) and public-health interventions. Exceeding M 0, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally.