TED2006
Joe DeRisi: Solving medical mysteries
Joe DeRisi：利用病毒篩檢解決醫學謎團
Filmed:
Readability: 4.3
474,538 views
生化學家Joe DeRiSi提出利用病毒DNA來診斷病毒（及其造成的疾病）的新方法。他的工作或許能幫助我們了解瘧疾、SARS、禽流感，和每天約六成未能被診斷出的病毒感染。
Joe DeRisi - Biochemist
Joe DeRisi hunts for the genes that make us sick. At his lab, he works to understand the genome of Plasmodium falciparum, the deadliest form of malaria. Full bio
Joe DeRisi hunts for the genes that make us sick. At his lab, he works to understand the genome of Plasmodium falciparum, the deadliest form of malaria. Full bio
Double-click the English transcript below to play the video.
00:12
How can we investigate
0
0
3000
我們該如何研究
00:15
this flora of viruses that surround us, and aid medicine?
1
3000
5000
這些在我們四周的病毒，以解決它們造成的健康問題？
00:20
How can we turn our cumulative knowledge of virology
2
8000
4000
我們要如何將累積許久的病毒學知識
00:24
into a simple, hand-held, single diagnostic assay?
3
12000
4000
轉化成簡單的掌上型單一檢驗法？
00:28
I want to turn everything we know right now about detecting viruses
4
16000
3000
我希望能將我們現在對檢驗病毒
00:31
and the spectrum of viruses that are out there
5
19000
2000
及生活中各種病毒基因的所有了解
00:33
into, let's say, a small chip.
6
21000
3000
轉變成一個小小的晶片。
00:36
When we started thinking about this project --
7
24000
2000
當我們開始思索這個計畫─
00:38
how we would make a single diagnostic assay
8
26000
3000
也就是我們要發展能同時偵測
00:41
to screen for all pathogens simultaneously --
9
29000
3000
所有病原體的單一檢驗法─
00:44
well, there's some problems with this idea.
10
32000
2000
這個想法遇到了一些問題。
00:46
First of all, viruses are pretty complex,
11
34000
4000
首先，病毒很複雜，
00:50
but they're also evolving very fast.
12
38000
4000
但他們卻也進化得非常快速。
00:54
This is a picornavirus.
13
42000
1000
這是小核醣酸病毒。
00:55
Picornaviruses -- these are things that include
14
43000
2000
它們會造成
00:57
the common cold and polio, things like this.
15
45000
3000
感冒、小兒麻痺這類傳染病。
01:00
You're looking at the outside shell of the virus,
16
48000
2000
你現在看到的是病毒的外鞘，
01:02
and the yellow color here are those parts of the virus
17
50000
3000
黃色區域演化得
01:05
that are evolving very, very fast,
18
53000
2000
非常、非常快，
01:07
and the blue parts are not evolving very fast.
19
55000
2000
而藍色區域則沒有那麼快。
01:09
When people think about making pan-viral detection reagents,
20
57000
3000
當我們想製造全病毒偵測試劑時，
01:12
usually it's the fast-evolving problem that's an issue,
21
60000
4000
病毒的快速演化是一個棘手問題，
01:16
because how can we detect things if they're always changing?
22
64000
2000
原因是我們要怎麼檢驗一個不斷在改變的東西呢？
01:18
But evolution is a balance:
23
66000
2000
但是演化是一種平衡：
01:20
where you have fast change, you also have ultra-conservation --
24
68000
4000
在某段基因序列快速改變的同時，也有序列超級保守─
01:24
things that almost never change.
25
72000
2000
幾乎不怎麼改變。
01:26
And so we looked into this a little more carefully,
26
74000
3000
我將提供各位一些數據，
01:29
and I'm going to show you data now.
27
77000
1000
讓大家再仔細研究一下。
01:30
This is just some stuff you can do on the computer from the desktop.
28
78000
3000
結果都是各位可以利用桌上型電腦自行計算得來。
01:33
I took a bunch of these small picornaviruses,
29
81000
2000
我分析了幾種小核醣酸病毒的基因，
01:35
like the common cold, like polio and so on,
30
83000
2000
像是感冒病毒、小兒麻痺病毒之類的，
01:37
and I just broke them down into small segments.
31
85000
4000
將它們分割成小片段，
01:41
And so took this first example, which is called coxsackievirus,
32
89000
3000
這是第一個例子：克沙奇病毒
01:44
and just break it into small windows.
33
92000
2000
將其基因分割成幾個小片段。
01:46
And I'm coloring these small windows blue
34
94000
2000
如果別種病毒跟克沙奇的
01:48
if another virus shares an identical sequence in its genome
35
96000
5000
某段基因序列一樣的話我就把這段基因
01:53
to that virus.
36
101000
1000
染成藍色。
01:54
These sequences right up here --
37
102000
2000
像這邊這個序列─
01:56
which don't even code for protein, by the way --
38
104000
2000
這序列並不會轉譯成蛋白質喔─
01:58
are almost absolutely identical across all of these,
39
106000
3000
在所有病毒裡幾乎都是一樣的，
02:01
so I could use this sequence as a marker
40
109000
4000
所以，我可以將這個序列當成標記
02:05
to detect a wide spectrum of viruses,
41
113000
2000
用來偵測許多不同的病毒，
02:07
without having to make something individual.
42
115000
3000
而不需要製造一些個別的序列。
02:10
Now, over here there's great diversity:
43
118000
2000
這裡，是歧異度高的序列：
02:12
that's where things are evolving fast.
44
120000
2000
也就是快速演化的序列。
02:14
Down here you can see slower evolution: less diversity.
45
122000
4000
這裡，則是演化較慢的序列，歧異度較低。
02:18
Now, by the time we get out here to, let's say,
46
126000
2000
現在，我們來看看
02:20
acute bee paralysis virus --
47
128000
2000
急性蜜蜂麻痺病毒─
02:22
probably a bad one to have if you're a bee ---
48
130000
2000
如果你是一隻蜜蜂你應該不想得到─
02:24
this virus shares almost no similarity to coxsackievirus,
49
132000
5000
這種病毒一點也不像克沙奇病毒，
02:29
but I can guarantee you that the sequences that are most conserved
50
137000
4000
但我跟妳保證，這隻病毒基因序列的
02:33
among these viruses on the right-hand of the screen
51
141000
2000
保留區段，也就是螢幕右端這邊
02:35
are in identical regions right up here.
52
143000
3000
跟這裡的克沙奇病毒基因序列一樣。
02:38
And so we can encapsulate these regions of ultra-conservation
53
146000
3000
所以，我們可以擷取這些在演化時不變動的
02:41
through evolution -- how these viruses evolved --
54
149000
3000
超級保守區段 ─ 這些病毒如何演化 ─
02:44
by just choosing DNA elements or RNA elements
55
152000
3000
將其中的DNA或RNA片段
02:47
in these regions to represent on our chip as detection reagents.
56
155000
4000
放在我們的晶片上當作檢驗試劑。
02:51
OK, so that's what we did, but how are we going to do that?
57
159000
3000
好，這就是我們的成果，可是我們下一步該怎麼做呢？
02:54
Well, for a long time, since I was in graduate school,
58
162000
2000
很久以前，當我還在念研究所時，
02:56
I've been messing around making DNA chips --
59
164000
3000
曾有一段時間忙亂地製作DNA晶片─
02:59
that is, printing DNA on glass.
60
167000
2000
也就是，把DNA印在玻璃上。
03:01
And that's what you see here:
61
169000
1000
就像你見到的這樣：
03:02
These little salt spots are just DNA tacked onto glass,
62
170000
3000
這些似鹽的小顆粒，就是印在玻璃上的DNA，
03:05
and so I can put thousands of these on our glass chip
63
173000
3000
我可以將數以千計的DNA碎片印上去
03:08
and use them as a detection reagent.
64
176000
2000
用來當作檢驗試劑。
03:10
We took our chip over to Hewlett-Packard
65
178000
2000
把我們的晶片拿到HP去
03:12
and used their atomic force microscope on one of these spots,
66
180000
2000
用他們的原子力顯微鏡觀察晶片上的一點，
03:14
and this is what you see:
67
182000
2000
你會看到：
03:16
you can actually see the strands of DNA lying flat on the glass here.
68
184000
3000
在玻璃上見到一層薄薄鍍上的DNA部分。
03:19
So, what we're doing is just printing DNA on glass --
69
187000
3000
我們所做的就是，把DNA印到玻璃上 --
03:22
little flat things -- and these are going to be markers for pathogens.
70
190000
4000
而病原體的標記，就會是這薄薄一層DNA。
03:26
OK, I make little robots in lab to make these chips,
71
194000
3000
我在實驗室做了一個小機器人來製造這些晶片，
03:29
and I'm really big on disseminating technology.
72
197000
3000
我實在太喜歡散播科技新知了。
03:32
If you've got enough money to buy just a Camry,
73
200000
3000
如果你有錢買Toyota Camry，
03:35
you can build one of these too,
74
203000
2000
你也能做一台DNA印表機，
03:37
and so we put a deep how-to guide on the Web, totally free,
75
205000
4000
我們把機器人的製作詳解放上網路，完全免費，
03:41
with basically order-off-the-shelf parts.
76
209000
2000
零件都是些市面上買的東西 --
03:43
You can build a DNA array machine in your garage.
77
211000
3000
你可以在自家車庫做一個DNA印表機。
03:46
Here's the section on the all-important emergency stop switch.
78
214000
3000
這是最重要的緊急停止開關。
03:49
(Laughter)
79
217000
2000
（笑）
03:51
Every important machine's got to have a big red button.
80
219000
3000
每一台重要的機器都得有個大紅按鈕。
03:54
But really, it's pretty robust.
81
222000
2000
說真的，這是一台美麗的機器。
03:56
You can actually be making DNA chips in your garage
82
224000
3000
你也可以在自家車庫製造DNA晶片，
03:59
and decoding some genetic programs pretty rapidly. It's a lot of fun.
83
227000
4000
快速的執行一些基因解碼計畫。這個很好玩。
04:03
(Laughter)
84
231000
1000
（笑）
04:04
And so what we did -- and this is a really cool project --
85
232000
4000
我們所做的 -- 這是個很酷的計畫 --
04:08
we just started by making a respiratory virus chip.
86
236000
2000
我們才剛剛開始製造呼吸道疾病相關病毒的晶片。
04:10
I talked about that --
87
238000
2000
我說就是為了那些，
04:12
you know, that situation where you go into the clinic
88
240000
2000
當你上診所看醫生，
04:14
and you don't get diagnosed?
89
242000
2000
卻沒有確切診斷的情況。
04:16
Well, we just put basically all the human respiratory viruses
90
244000
2000
所以我們把所有人類呼吸道疾病相關病毒的序列
04:18
on one chip, and we threw in herpes virus for good measure --
91
246000
3000
放上晶片，然後用單純皰疹病毒測試 --
04:21
I mean, why not?
92
249000
1000
為何不呢？
04:22
The first thing you do as a scientist is,
93
250000
2000
當科學家，首先要做的是，
04:24
you make sure stuff works.
94
252000
1000
確定主意行得通。
04:25
And so what we did is, we take tissue culture cells
95
253000
3000
所以我們培養了一些細胞
04:28
and infect them with various viruses,
96
256000
2000
以各種病毒感染，
04:30
and we take the stuff and fluorescently label the nucleic acid,
97
258000
4000
再用螢光標定核酸，
04:34
the genetic material that comes out of these tissue culture cells --
98
262000
3000
也就是那些培養的細胞釋出的基因物質 --
04:37
mostly viral stuff -- and stick it on the array to see where it sticks.
99
265000
4000
其中大部分的是病毒 -- 將這些核酸片段放上晶片。
04:41
Now, if the DNA sequences match, they'll stick together,
100
269000
2000
如果DNA序列相合，兩者就會相黏，
04:43
and so we can look at spots.
101
271000
2000
這時我們可以觀察螢光分布。
04:45
And if spots light up, we know there's a certain virus in there.
102
273000
2000
晶片上的螢光點，就代表某病毒的存在。
04:47
That's what one of these chips really looks like,
103
275000
2000
這是晶片實際的樣子，
04:49
and these red spots are, in fact, signals coming from the virus.
104
277000
3000
這些紅點實際上就代表病毒訊號。
04:52
And each spot represents a different family of virus
105
280000
3000
每一個點是不同的病毒家族
04:55
or species of virus.
106
283000
1000
或是病毒種類。
04:56
And so, that's a hard way to look at things,
107
284000
2000
因為這樣不太容易觀察，
04:58
so I'm just going to encode things as a little barcode,
108
286000
2000
所以我將結果用小條碼表示，
05:00
grouped by family, so you can see the results in a very intuitive way.
109
288000
4000
將病毒各家族分類，以較直觀的方式呈現。
05:04
What we did is, we took tissue culture cells
110
292000
2000
我們把培養的細胞
05:06
and infected them with adenovirus,
111
294000
2000
以腺病毒感染，
05:08
and you can see this little yellow barcode next to adenovirus.
112
296000
4000
你就會在腺病毒旁看到黃色小條碼。
05:12
And, likewise, we infected them with parainfluenza-3 --
113
300000
3000
同理，我們以副流感病毒三型感染細胞 --
05:15
that's a paramyxovirus -- and you see a little barcode here.
114
303000
2000
這是一種副黏液病毒 -- 小條碼在這裡。
05:17
And then we did respiratory syncytial virus.
115
305000
3000
我們也試過呼吸道融合瘤病毒。
05:20
That's the scourge of daycare centers everywhere --
116
308000
2000
這種病毒在養護中心大規模傳染 --
05:22
it's like boogeremia, basically.
117
310000
2000
它就像是充斥於血液中的鼻屎一般。
05:24
(Laughter)
118
312000
1000
（笑）
05:25
You can see that this barcode is the same family,
119
313000
4000
你可能注意到，這個條碼跟造成嚴重感冒的
05:29
but it's distinct from parainfluenza-3,
120
317000
2000
副流感病毒三型屬同一家族，
05:31
which gives you a very bad cold.
121
319000
2000
但兩者不太一樣。
05:33
And so we're getting unique signatures, a fingerprint for each virus.
122
321000
3000
這讓我們有每一種病毒的獨特標識，就像指紋一般。
05:36
Polio and rhino: they're in the same family, very close to each other.
123
324000
3000
小兒麻痺病毒跟鼻病毒同屬一個家族，非常接近。
05:39
Rhino's the common cold, and you all know what polio is,
124
327000
2000
後者造成感冒，而你們都知道小兒麻痺是甚麼，
05:41
and you can see that these signatures are distinct.
125
329000
3000
你可以看到，它們的標識不太一樣。
05:44
And Kaposi's sarcoma-associated herpes virus
126
332000
3000
這是卡波西氏肉瘤病毒
05:47
gives a nice signature down here.
127
335000
2000
它們提供了清晰的標識。
05:49
And so it is not any one stripe or something
128
337000
2000
所以，這不是一個只會告訴我
05:51
that tells me I have a virus of a particular type here;
129
339000
2000
這是哪一種病毒的條紋；
05:53
it's the barcode that in bulk represents the whole thing.
130
341000
4000
而是一個可以表達病毒全貌的條碼。
05:57
All right, I can see a rhinovirus --
131
345000
2000
好吧，我看到鼻病毒 --
05:59
and here's the blow-up of the rhinovirus's little barcode --
132
347000
2000
這裡是鼻病毒的小條碼 --
06:01
but what about different rhinoviruses?
133
349000
2000
但是鼻病毒有許多亞型
06:03
How do I know which rhinovirus I have?
134
351000
2000
我怎麼知道我得了哪一種？
06:05
There're 102 known variants of the common cold,
135
353000
3000
以知約有102種病毒會造成普通感冒，
06:08
and there're only 102 because people got bored collecting them:
136
356000
3000
只有102種是因為人們懶得再增加感冒病毒的名單長度了：
06:11
there are just new ones every year.
137
359000
2000
每一年我們都會發現新種感冒病毒。
06:13
And so, here are four different rhinoviruses,
138
361000
2000
這裡有四種不同的鼻病毒，
06:15
and you can see, even with your eye,
139
363000
2000
你看，單純只靠肉眼，
06:17
without any fancy computer pattern-matching
140
365000
2000
即使沒有高科技的電腦軟體
06:19
recognition software algorithms,
141
367000
2000
計算模式跟配對，
06:21
that you can distinguish each one of these barcodes from each other.
142
369000
3000
你也可以分辨出每一組條碼。
06:24
Now, this is kind of a cheap shot,
143
372000
2000
現在，這是個便宜的診斷方法，
06:26
because I know what the genetic sequence of all these rhinoviruses is,
144
374000
3000
因為我知道所有鼻病毒的基因序列，
06:29
and I in fact designed the chip
145
377000
1000
所以我設計了可以分辨
06:30
expressly to be able to tell them apart,
146
378000
2000
這些不同鼻病毒的晶片，
06:32
but what about rhinoviruses that have never seen a genetic sequencer?
147
380000
4000
但那些不知道序列的鼻病毒該怎麼辦呢？
06:36
We don't know what the sequence is; just pull them out of the field.
148
384000
2000
我們不知道它們的序列，就先擱置它們。
06:38
So, here are four rhinoviruses
149
386000
2000
這是四種鼻病毒
06:40
we never knew anything about --
150
388000
2000
我們對它們一無所知 --
06:42
no one's ever sequenced them -- and you can also see
151
390000
3000
沒人知道它們的序列 -- 而你可見
06:45
that you get unique and distinguishable patterns.
152
393000
2000
它們的樣本仍獨特可辨。
06:47
You can imagine building up some library, whether real or virtual,
153
395000
3000
你或許想到了，我們可以設個資料庫，
06:50
of fingerprints of essentially every virus.
154
398000
2000
包含絕大多數病毒的實際或虛擬指紋。
06:52
But that's, again, shooting fish in a barrel, you know, right?
155
400000
3000
這就跟囊中取物一樣容易，對吧？
06:55
You have tissue culture cells. There are a ton of viruses.
156
403000
2000
你培植的細胞裡頭有一大堆病毒。
06:57
What about real people?
157
405000
2000
但在人身上的真實情況呢？
06:59
You can't control real people, as you probably know.
158
407000
2000
你大概已經知道，我們不能控制真人。
07:01
You have no idea what someone's going to cough into a cup,
159
409000
4000
我們不知道人們會把甚麼咳進樣本杯裡，
07:05
and it's probably really complex, right?
160
413000
3000
情況大概會很複雜吧？
07:08
It could have lots of bacteria, it could have more than one virus,
161
416000
3000
可能會有很多細菌，或超過一種以上的病毒，
07:11
and it certainly has host genetic material.
162
419000
2000
當然還會有宿主的基因物質，
07:13
So how do we deal with this?
163
421000
1000
我們該怎樣處理這樣的情況？
07:14
And how do we do the positive control here?
164
422000
2000
這時該怎麼設計陽性對照組？
07:16
Well, it's pretty simple.
165
424000
2000
這個頗容易。
07:18
That's me, getting a nasal lavage.
166
426000
2000
這是我，在接受鼻灌洗。
07:20
And the idea is, let's experimentally inoculate people with virus.
167
428000
5000
這個概念是，我們在人體接種病毒，
07:25
This is all IRB-approved, by the way; they got paid.
168
433000
5000
這個實驗是醫學研究倫理委員會核准的；岔題一下，他們有領薪水。
07:30
And basically we experimentally inoculate people
169
438000
3000
這個實驗是：我們實驗在人體接種
07:33
with the common cold virus.
170
441000
1000
一般感冒病毒。
07:34
Or, even better, let's just take people
171
442000
2000
但，更好的方法是，我們從
07:36
right out of the emergency room --
172
444000
1000
急診室收集病人 --
07:37
undefined, community-acquired respiratory tract infections.
173
445000
4000
那些病原不明，社區型呼吸道感染的病人。
07:41
You have no idea what walks in through the door.
174
449000
2000
你不知道誰會走過急診室那扇門。
07:43
So, let's start off with the positive control first,
175
451000
3000
從陽性對照組開始，
07:46
where we know the person was healthy.
176
454000
2000
我們知道他們原本是健康的。
07:48
They got a shot of virus up the nose,
177
456000
2000
我們把病毒打進鼻子裡，
07:50
let's see what happens.
178
458000
1000
看看會如何。
07:51
Day zero: nothing happening.
179
459000
2000
實驗剛開始：甚麼都沒發生。
07:53
They're healthy; they're clean -- it's amazing.
180
461000
2000
受試者很健康，沒有受感染 -- 這真是神奇。
07:55
Actually, we thought the nasal tract might be full of viruses
181
463000
2000
其實，我們以為這時鼻腔應該充滿病毒了
07:57
even when you're walking around healthy.
182
465000
1000
即使你是健步如飛的健康人士。
07:58
It's pretty clean. If you're healthy, you're pretty healthy.
183
466000
2000
鼻腔很乾淨。如果你是健康的，實際上也相當健康。
08:00
Day two: we get a very robust rhinovirus pattern,
184
468000
4000
第二天：樣本出現大量鼻病毒的指紋，
08:04
and it's very similar to what we get in the lab
185
472000
2000
跟我們在實驗室裡收集的
08:06
doing our tissue culture experiment.
186
474000
1000
含病毒的細胞一樣。
08:07
So that's great, but again, cheap shot, right?
187
475000
3000
真好，正如我們說過，是個便宜的解法，對吧？
08:10
We put a ton of virus up this guy's nose. So --
188
478000
2000
我們讓這傢伙的鼻腔裡都是病毒了。所以 --
08:12
(Laughter)
189
480000
1000
（笑）
08:13
-- I mean, we wanted it to work. He really had a cold.
190
481000
4000
我是說，我們希望實驗成功。我是說，這傢伙感冒了。
08:17
So, how about the people who walk in off the street?
191
485000
4000
那麼，那些從大街上進來的人呢？
08:21
Here are two individuals represented by their anonymous ID codes.
192
489000
2000
這是兩位不知名實驗者的病毒指紋。
08:23
They both have rhinoviruses; we've never seen this pattern in lab.
193
491000
4000
他們都有鼻病毒，其指紋卻跟實驗室的病毒株不同。
08:27
We sequenced part of their viruses;
194
495000
2000
我們分析了病毒的基因序列；
08:29
they're new rhinoviruses no one's actually even seen.
195
497000
3000
發現他們身上的病毒是新品種。
08:32
Remember, our evolutionary-conserved sequences
196
500000
2000
記住，我們使用在演化過程中恆定的保留序列
08:34
we're using on this array allow us to detect
197
502000
2000
來進行檢測，這讓我們得以
08:36
even novel or uncharacterized viruses,
198
504000
2000
找到新種或未分析過的病毒，
08:38
because we pick what is conserved throughout evolution.
199
506000
4000
這都是因為我們選擇保留序列的緣故。
08:42
Here's another guy. You can play the diagnosis game yourself here.
200
510000
3000
這是另一個受試者。你也可以自己玩檢驗遊戲。
08:45
These different blocks represent
201
513000
2000
這些不同的指紋群代表
08:47
the different viruses in this paramyxovirus family,
202
515000
2000
副黏膜病毒家族裡不同的病毒株，
08:49
so you can kind of go down the blocks
203
517000
1000
你可以分析這些指紋組
08:50
and see where the signal is.
204
518000
2000
看看是哪些你認識的病毒。
08:52
Well, doesn't have canine distemper; that's probably good.
205
520000
3000
嗯，沒有犬瘟熱，還不錯。
08:55
(Laughter)
206
523000
2000
（笑）
08:57
But by the time you get to block nine,
207
525000
2000
但是當你分析到第九組，
08:59
you see that respiratory syncytial virus.
208
527000
2000
你看到呼吸道融合瘤病毒。
09:01
Maybe they have kids. And then you can see, also,
209
529000
3000
這表示病人可能有小孩。同時你得知，
09:04
the family member that's related: RSVB is showing up here.
210
532000
2000
病原是呼吸道融合瘤病毒B亞群。
09:06
So, that's great.
211
534000
1000
所以，這個很不錯。
09:07
Here's another individual, sampled on two separate days --
212
535000
3000
這是另一個病人，在兩個不同的診次 --
09:10
repeat visits to the clinic.
213
538000
2000
收集了兩次樣本。
09:12
This individual has parainfluenza-1,
214
540000
3000
他感染了副流感病毒第一型，
09:15
and you can see that there's a little stripe over here
215
543000
2000
樣本中也有微量仙台病毒的指紋：
09:17
for Sendai virus: that's mouse parainfluenza.
216
545000
3000
這是老鼠身上的副流感病毒。
09:20
The genetic relationships are very close there. That's a lot of fun.
217
548000
4000
這兩種病毒的基因很接近。很有趣吧！
09:24
So, we built out the chip.
218
552000
1000
於是，我們製造了晶片。
09:25
We made a chip that has every known virus ever discovered on it.
219
553000
4000
晶片上有所有已經被發現的病毒基因片斷。
09:29
Why not? Every plant virus, every insect virus, every marine virus.
220
557000
3000
那麼，何不也來做所有的植物病毒、昆蟲病毒、海水病毒的晶片。
09:32
Everything that we could get out of GenBank --
221
560000
2000
反正相關資訊都在GenBank --
09:34
that is, the national repository of sequences.
222
562000
2000
也就是基因序列的國家型資料庫裡頭了。
09:36
Now we're using this chip. And what are we using it for?
223
564000
3000
既然我們手上有晶片，能用它來幹嘛？
09:39
Well, first of all, when you have a big chip like this,
224
567000
2000
首先，當我們有像這樣的大晶片時，
09:41
you need a little bit more informatics,
225
569000
2000
還需要一些其他的資訊，
09:43
so we designed the system to do automatic diagnosis.
226
571000
2000
來設計能夠進行自動診斷的系統。
09:45
And the idea is that we simply have virtual patterns,
227
573000
3000
我們只有虛擬的樣本 --
09:48
because we're never going to get samples of every virus --
228
576000
2000
因為我們從來就無法收集所有病毒的樣本；
09:50
it would be virtually impossible. But we can get virtual patterns,
229
578000
3000
就算如此，我們還是能得到虛擬的樣本，
09:53
and compare them to our observed result --
230
581000
2000
將之與觀察到的結果比較，
09:55
which is a very complex mixture -- and come up with some sort of score
231
583000
4000
因為是複雜的混合物，我們能計算出
09:59
of how likely it is this is a rhinovirus or something.
232
587000
3000
代表樣本含鼻病毒或是其他病毒可能性的分數。
10:02
And this is what this looks like.
233
590000
2000
看起來就像這樣。
10:04
If, for example, you used a cell culture
234
592000
2000
舉例來說，你用被
10:06
that's chronically infected with papilloma,
235
594000
2000
乳突病毒感染的細胞，
10:08
you get a little computer readout here,
236
596000
2000
得到這樣的數據，
10:10
and our algorithm says it's probably papilloma type 18.
237
598000
4000
而我們的程式說它可能是第十八型。
10:14
And that is, in fact, what these particular cell cultures
238
602000
2000
這也的確是實驗室裡的細胞
10:16
are chronically infected with.
239
604000
2000
感染的病毒亞型。
10:18
So let's do something a little bit harder.
240
606000
2000
我們來做個難一點的實驗
10:20
We put the beeper in the clinic.
241
608000
1000
在診間裡放個傳呼機。
10:21
When somebody shows up, and the hospital doesn't know what to do
242
609000
3000
在醫院無法做出診斷，不知道要對病人
10:24
because they can't diagnose it, they call us.
243
612000
2000
做甚麼處置時，他們會聯繫我們。
10:26
That's the idea, and we're setting this up in the Bay Area.
244
614000
2000
計畫是：我們在灣區設置這個系統。
10:28
And so, this case report happened three weeks ago.
245
616000
2000
這是三周前的病例。
10:30
We have a 28-year-old healthy woman, no travel history,
246
618000
3000
有個28歲的健康女性，
10:33
[unclear], doesn't smoke, doesn't drink.
247
621000
3000
【不清楚】，無外出旅遊記錄，不菸，不酒。
10:36
10-day history of fevers, night sweats, bloody sputum --
248
624000
4000
發燒、夜間盜汗、痰中有血，也就是咳血、
10:40
she's coughing up blood -- muscle pain.
249
628000
2000
肌肉痛，長達十天。
10:42
She went to the clinic, and they gave her antibiotics
250
630000
4000
她就醫求助，拿了抗生素，
10:46
and then sent her home.
251
634000
1000
然後回家。
10:47
She came back after ten days of fever, right? Still has the fever,
252
635000
4000
十天後她又回來，仍在發燒 --
10:51
and she's hypoxic -- she doesn't have much oxygen in her lungs.
253
639000
3000
而且缺氧 -- 她的肺部含氧量低。
10:54
They did a CT scan.
254
642000
1000
他們為她照了電腦斷層掃描。
10:55
A normal lung is all sort of dark and black here.
255
643000
4000
正常的肺應該略為色暗或呈黑色。
10:59
All this white stuff -- it's not good.
256
647000
2000
但是她的卻是白色，狀況不妙。
11:01
This sort of tree and bud formation indicates there's inflammation;
257
649000
3000
影像中的枝狀和芽狀散佈表示肺部發炎；
11:04
there's likely to be infection.
258
652000
2000
有可能是感染。
11:06
OK. So, the patient was treated then
259
654000
3000
於是，病人開始接受抗生素治療
11:09
with a third-generation cephalosporin antibiotic and doxycycline,
260
657000
4000
包括第三代頭孢霉素，還有多西環素，
11:13
and on day three, it didn't help: she had progressed to acute failure.
261
661000
4000
到第三天仍沒有起色，她出現急性呼吸衰竭的症狀。
11:17
They had to intubate her, so they put a tube down her throat
262
665000
3000
醫生得為她插管，
11:20
and they began to mechanically ventilate her.
263
668000
1000
接上呼吸器。
11:21
She could no longer breathe for herself.
264
669000
2000
她無法自主呼吸。
11:23
What to do next? Don't know.
265
671000
2000
然後呢？不知道。
11:25
Switch antibiotics: so they switched to another antibiotic,
266
673000
3000
改抗生素，於是醫生改用其他抗生素，
11:28
Tamiflu.
267
676000
2000
用特敏福，而 --
11:30
It's not clear why they thought she had the flu,
268
678000
2000
他們卻不太清楚為什麼醫生認為她得了感冒 --
11:32
but they switched to Tamiflu.
269
680000
2000
但仍使轉用特敏福。
11:34
And on day six, they basically threw in the towel.
270
682000
2000
第六天，他們認輸。
11:36
You do an open lung biopsy when you've got no other options.
271
684000
4000
在別無選擇的狀況下，開胸取活組織组樣本檢查。
11:40
There's an eight percent mortality rate with just doing this procedure,
272
688000
2000
手術的致命率為百分之八，
11:42
and so basically -- and what do they learn from it?
273
690000
3000
大體上，他們因此得到什麼？
11:45
You're looking at her open lung biopsy.
274
693000
2000
你們看到的是她的開胸活組織樣本。
11:47
And I'm no pathologist, but you can't tell much from this.
275
695000
2000
我不是病理學家，但從這個你也看不出來甚麼。
11:49
All you can tell is, there's a lot of swelling: bronchiolitis.
276
697000
3000
唯一能辨別的就是：有很多腫脹、支氣管炎。
11:52
It was "unrevealing": that's the pathologist's report.
277
700000
3000
病理學家的報告沒甚麼新發現。
11:55
And so, what did they test her for?
278
703000
3000
既然如此，他們檢驗了她什麼？
11:58
They have their own tests, of course,
279
706000
1000
當然，他們有自己的檢查，
11:59
and so they tested her for over 70 different assays,
280
707000
3000
超過七十種不同的方法，
12:02
for every sort of bacteria and fungus and viral assay
281
710000
3000
所有能在市面上找到的
12:05
you can buy off the shelf:
282
713000
2000
細菌、黴菌、病毒測試：
12:07
SARS, metapneumovirus, HIV, RSV -- all these.
283
715000
3000
包括非典型病毒、間質肺炎病毒、愛滋病毒、呼吸融合瘤病毒 -- 等等。
12:10
Everything came back negative, over 100,000 dollars worth of tests.
284
718000
4000
檢查結果都是陰性，花了十萬多美金。
12:14
I mean, they went to the max for this woman.
285
722000
3000
我是說，醫生為了她已經盡了一切努力。
12:17
And basically on hospital day eight, that's when they called us.
286
725000
3000
直到第八天，院方連繫我們。
12:20
They gave us endotracheal aspirate --
287
728000
2000
給我們病人氣管內的抽取物 --
12:22
you know, a little fluid from the throat,
288
730000
2000
也就是在喉嚨裡放根管子，
12:24
from this tube that they got down there -- and they gave us this.
289
732000
2000
抽取喉嚨裡少少的液體。
12:26
We put it on the chip; what do we see? Well, we saw parainfluenza-4.
290
734000
5000
我們將樣本放上晶片，觀察到的是：副流感病毒第四型。
12:31
Well, what the hell's parainfluenza-4?
291
739000
2000
這個副流感病毒第四型是甚麼鬼東西？
12:33
No one tests for parainfluenza-4. No one cares about it.
292
741000
3000
沒有人測過這隻病毒，沒人想到過它。
12:36
In fact, it's not even really sequenced that much.
293
744000
3000
事實上，我們甚至連病毒的完整序都沒有。
12:39
There's just a little bit of it sequenced.
294
747000
2000
只有一小段序列。
12:41
There's almost no epidemiology or studies on it.
295
749000
2000
也沒甚麼流行病學研究過它。
12:43
No one would even consider it,
296
751000
2000
沒有人想過它，
12:45
because no one had a clue that it could cause respiratory failure.
297
753000
3000
因為沒人有證據證明，這病毒會造成呼吸衰竭。
12:48
And why is that? Just lore. There's no data --
298
756000
3000
為什麼？只有口頭知識，卻沒有數據 --
12:51
no data to support whether it causes severe or mild disease.
299
759000
4000
沒有數據證明這種病毒會造成嚴重或輕微的疾病。
12:55
Clearly, we have a case of a healthy person that's going down.
300
763000
3000
顯然，我們手上有個正在失去生命的健康人。
12:58
OK, that's one case report.
301
766000
3000
這是個病例報告。
13:01
I'm going to tell you one last thing in the last two minutes
302
769000
2000
最後兩分鐘，告訴大家一個
13:03
that's unpublished -- it's going to come out tomorrow --
303
771000
3000
還沒有發表的研究：論文明天才會出來 --
13:06
and it's an interesting case of how you might use this chip
304
774000
3000
這個有趣例子告訴我們，我們可以如何使用晶片
13:09
to find something new and open a new door.
305
777000
2000
來發現新事物、打開新知之門。
13:11
Prostate cancer. I don't need to give you many statistics
306
779000
4000
攝護腺癌 / 前列腺癌。我不需要報告太多關於
13:15
about prostate cancer. Most of you already know it:
307
783000
3000
攝護腺癌的統計，你們大部分都很清楚：
13:18
third leading cause of cancer deaths in the U.S.
308
786000
2000
（攝護腺癌）在美國癌症死亡率中排名第三。
13:20
Lots of risk factors,
309
788000
2000
有許多致病風險，
13:22
but there is a genetic predisposition to prostate cancer.
310
790000
4000
其中一項引致攝護腺癌的成因為基因傾向。
13:26
For maybe about 10 percent of prostate cancer,
311
794000
2000
大概有一成的攝護腺癌病人，
13:28
there are folks that are predisposed to it.
312
796000
2000
帶著這種傾向的基因。
13:30
And the first gene that was mapped in association studies
313
798000
4000
相關研究中找到的第一個早期攝護腺癌
13:34
for this, early-onset prostate cancer, was this gene called RNASEL.
314
802000
4000
致癌基因，叫做RNASEL。
13:38
What is that? It's an antiviral defense enzyme.
315
806000
3000
這是甚麼？是一種對抗病毒感染的酵素。
13:41
So, we're sitting around and thinking,
316
809000
2000
於是我們坐下思考，
13:43
"Why would men who have the mutation --
317
811000
2000
為什麼一個人，帶著有缺陷的
13:45
a defect in an antiviral defense system -- get prostate cancer?
318
813000
5000
抗病毒感染系統，會得攝護腺癌？
13:50
It doesn't make sense -- unless, maybe, there's a virus?"
319
818000
3000
這一點道理也沒有。除非，可能，病毒會造成攝護腺癌。
13:53
So, we put tumors --- and now we have over 100 tumors -- on our array.
320
821000
6000
於是，我們把腫瘤 -- 現在我們有至少一百個腫瘤 -- 的基因放到晶片上。
13:59
And we know who's got defects in RNASEL and who doesn't.
321
827000
3000
我們知道誰有RNASEL缺陷，誰沒有。
14:02
And I'm showing you the signal from the chip here,
322
830000
3000
我現在把晶片所傳達的訊息展示出來，
14:05
and I'm showing you for the block of retroviral oligos.
323
833000
4000
還有這個代表反轉錄病毒引子的區塊。
14:09
And what I'm telling you here from the signal, is
324
837000
2000
而這個訊息告訴我們，
14:11
that men who have a mutation in this antiviral defense enzyme,
325
839000
4000
帶著抗病毒系統變異、
14:15
and have a tumor, often have -- 40 percent of the time --
326
843000
4000
具攝護腺癌的男性，通常 -- 其中四成 --
14:19
a signature which reveals a new retrovirus.
327
847000
4000
帶有新種逆轉濾過性病毒特徵。
14:23
OK, that's pretty wild. What is it?
328
851000
3000
很出乎意料吧？這到底是什麼？
14:26
So, we clone the whole virus.
329
854000
1000
於是，我們複製了整個病毒。
14:27
First of all, I'll tell you that a little automated prediction told us
330
855000
4000
首先，一個小小的自動預測告訴我們
14:31
it was very similar to a mouse virus.
331
859000
2000
這隻病毒非常接近老鼠病毒。
14:33
But that doesn't tell us too much,
332
861000
1000
但這不能告訴我們甚麼，
14:34
so we actually clone the whole thing.
333
862000
2000
所以我們實質上複製了整個病毒。
14:36
And the viral genome I'm showing you right here?
334
864000
2000
而我在這裡展出的病毒染色體序列
14:38
It's a classic gamma retrovirus, but it's totally new;
335
866000
3000
是典型的γ逆轉錄病毒屬，但它是全新的，
14:41
no one's ever seen it before.
336
869000
1000
從來沒有人發現過它。
14:42
Its closest relative is, in fact, from mice,
337
870000
3000
事實上，最接近的親屬是，老鼠的逆轉錄病毒，
14:45
and so we would call this a xenotropic retrovirus,
338
873000
4000
所以我們將之命名為異嗜性逆轉錄病毒，
14:49
because it's infecting a species other than mice.
339
877000
3000
因為它感染了除老鼠以外的物種。
14:52
And this is a little phylogenetic tree
340
880000
2000
這是它的小演化樹，
14:54
to see how it's related to other viruses.
341
882000
2000
可見這隻新病毒跟其他病毒的親緣關係。
14:56
We've done it for many patients now,
342
884000
3000
我們已經分析過許多病人，
14:59
and we can say that they're all independent infections.
343
887000
3000
發現這些全部都是獨立感染。
15:02
They all have the same virus,
344
890000
1000
他們都被同一種病毒感染，
15:03
but they're different enough that there's reason to believe
345
891000
3000
但他們不同處多到我們有理由相信
15:06
that they've been independently acquired.
346
894000
2000
他們都是獨立感染。
15:08
Is it really in the tissue? And I'll end up with this: yes.
347
896000
2000
病毒真的存在於組織中嗎？我以此作結。是的。
15:10
We take slices of these biopsies of tumor tissue
348
898000
3000
我們切片了許多腫瘤活組織
15:13
and use material to actually locate the virus,
349
901000
2000
利用試劑來找出病毒的位置，
15:15
and we find cells here with viral particles in them.
350
903000
4000
而我們發現這裡的細胞帶有病毒顆粒。
15:19
These guys really do have this virus.
351
907000
2000
這些病人身上的確有這種病毒。
15:21
Does this virus cause prostate cancer?
352
909000
2000
病毒會引致攝護腺癌嗎？
15:23
Nothing I'm saying here implies causality. I don't know.
353
911000
4000
我手上的資料並沒有顯示其中的因果關係。我不知道。
15:27
Is it a link to oncogenesis? I don't know.
354
915000
2000
它們跟癌變有關嗎？我不知道。
15:29
Is it the case that these guys are just more susceptible to viruses?
355
917000
4000
還是只是這些病人容易被病毒感染？
15:33
Could be. And it might have nothing to do with cancer.
356
921000
3000
可能吧。病毒也可能跟癌症一點關係都沒有。
15:36
But now it's a door.
357
924000
1000
但這是一扇門。
15:37
We have a strong association between the presence of this virus
358
925000
3000
我們找到病毒表現和基因突變
15:40
and a genetic mutation that's been linked to cancer.
359
928000
3000
跟癌症間的強烈關聯性。
15:43
That's where we're at.
360
931000
1000
這就是我們目前的研究進度。
15:44
So, it opens up more questions than it answers, I'm afraid,
361
932000
4000
不過，我恐怕，研究提供的問題比解答還多，
15:48
but that's what, you know, science is really good at.
362
936000
2000
但，這也是科學擅長的東西。
15:50
This was all done by folks in the lab --
363
938000
2000
這些研究是實驗室同仁共同努力的結果，
15:52
I cannot take credit for most of this.
364
940000
1000
我無法獨享榮耀。
15:53
This is a collaboration between myself and Don.
365
941000
1000
這是我跟Don合作的研究成果。
15:54
This is the guy who started the project in my lab,
366
942000
3000
這位在我的實驗室開始這個計畫，
15:57
and this is the guy who's been doing prostate stuff.
367
945000
2000
而這位一直進行攝護腺癌的研究。
15:59
Thank you very much. (Applause)
368
947000
3000
非常謝謝各位。
ABOUT THE SPEAKER
Joe DeRisi - BiochemistJoe DeRisi hunts for the genes that make us sick. At his lab, he works to understand the genome of Plasmodium falciparum, the deadliest form of malaria.
Why you should listen
Joseph DeRisi is a molecular biologist and biochemist, on the hunt for the genomic basis of illness. His lab at UCSF is focused on the cause of malaria, and he's also poked into SARS, avian flu and other new diseases as they crop up. His approach combines scientific rigor with a nerd's boundary-breaking enthusiasm for new techniques -- one of the qualities that helped him win a MacArthur "genius" grant in 2004. A self-confessed computer geek, DeRisi designed and programmed a groundbreaking tool for finding (and fighting) viruses -- the ViroChip, a DNA microarray that test for the presence of all known viruses in one step.
In 2008, DeRisi won the Heinz Award for Technology, the Economy and Employment.
Joe DeRisi | Speaker | TED.com